Forest change

This layer displays the geographic coverage of FORMA alerts, which
corresponds to the coverage of tropical and subtropical moist broadleaf
forests and tropical and subtropical dry broadleaf forests according
to WWF ecoregions.

FORMA GEOGRAPHIC COVERAGE

This layer displays the geographic coverage of FORMA alerts, which
corresponds to the coverage of tropical and subtropical moist broadleaf
forests and tropical and subtropical dry broadleaf forests according
to WWF ecoregions.

Tree Cover Canopy Density Settings

Drag the handle to adjust the minimum tree cover canopy (TCC) density for the visualization and analysis of Hansen/UMD/Google/USGS/NASA tree cover and tree cover loss. TCC density represents the estimated percent of a pixel that was covered by tree canopy in the year 2000, as determined from the analysis of satellite imagery. For the tree cover loss data, TCC density therefore corresponds to the density of tree cover before loss occurred. For example, if you select 25% as the minimum TCC density, you will only see tree cover loss pixels for which the original tree cover density was greater than 25%.

Adjustments to the minimum TCC density only affect the tree cover and tree cover loss data layers. This feature does not pertain to Hansen/UMD/Google/USGS/NASA tree cover gain or to other GFW data layers or statistics. Tree cover gain is displayed with a set minimum TCC density greater than 50%. The minimum TCC density cannot be changed independently for tree cover and tree cover loss. A change made to one data layer will immediately take effect in the other.

This feature is also available for statistics within the Country Profiles & Rankings. However, the adjustment made to the visualization and analysis through the map view will not be automatically reflected in other areas of the website. To adjust the minimum TCC density within the Country Profiles & Rankings pages, click on the settings icon.

Tree cover loss is not always deforestation

Loss of tree cover may occur for many reasons, including deforestation, fire, and logging within the course of sustainable forestry operations. In sustainably managed forests, the “loss” will eventually show up as “gain”, as young trees get large enough to achieve canopy closure.

Varies according to selection (use the legend on the map to change the minimum tree cover canopy density threshold)

Cautions

This data layer was updated in January 2015 to extend the tree cover loss analysis to 2013, and in August 2015 to extend the tree cover loss analysis to 2014. The updates include new data for the target year and re-processed data for the previous two years (2011 and 2012 for the 2013 update, 2012 and 2013 for the 2014 update). The re-processing increased the amount of change that could be detected, resulting in some changes in calculated tree cover loss for 2011-2013 compared to the previous versions. Calculated tree cover loss for 2001-2010 remains unchanged. The integrated use of the original 2001-2012 (Version 1.0) data and the updated 2011–2014 (Version 1.1) data should be performed with caution.

For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities. “Loss” indicates the removal or mortality of tree canopy cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.

When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).

Overview

This data set measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and approximately 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2014 interval. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss.

Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced.

2015 Update (Version 1.1)

This data set has been updated twice since its creation, and now includes loss up to 2014. The analysis method has been modified in numerous ways, and the update should be seen as part of a transition to a future “version 2.0” of this data set that is more consistent over the entire 2001 and onward period. Key changes include:

The use of Landsat 8 data for 2013-2014 and Landsat 5 data for 2011-2012

The reprocessing of data from the previous two years in measuring loss (2011 and 2012 for the 2013 update, 2012 and 2013 for the 2014 update)

Improved training data for calibrating the loss model

Improved per sensor quality assessment models to filter input data

Improved input spectral features for building and applying the loss model

These changes lead to a different and improved detection of global tree cover loss. However, the years preceding 2011 have not yet been reprocessed with the revised analysis methods, and users will notice inconsistencies between versions 1.0 (2001-2012) and 1.1 (2011-2014) as a result. It must also be noted that a full validation of the results incorporating Landsat 8 has not been undertaken. Such an analysis may reveal a more sensitive ability to detect and map forest disturbance using Landsat 8 data. If this is the case then there will be a more fundamental limitation to the consistency of this data set before and after the inclusion of Landsat 8 data. Validation of Landsat 8-incorporated loss detection is planned.

Some examples of improved change detection in the 2011–2014 update include the following:

These are examples of dynamics that may be differentially mapped over the 2011-2014 period in Version 1.1. A version 2.0 reprocessing of the 2001 and onward record is planned, but no delivery date is yet confirmed.

PRODES only identifies forest clearings of 6.25 hectares or larger, so forest degradation or smaller clearings from fire or selective logging are not detected.

Frequent cloud cover over areas of the Amazon may change the reported year of deforestation. The year reported is the first year deforestation is identified by analysts, but this does not necessarily correspond to the year of deforestation if the landscape has been covered by clouds in previous years.

Overview

The PRODES project monitors clear cut deforestation in the Brazilian Legal Amazon, and has produced annual deforestation rates for the region since 1988. The Brazilian government uses these figures to establish public policy, including defining access to credit in the Amazon biome, establishing deforestation reduction goals, and soliciting funds to reduce deforestation. PRODES historically used Landsat 5 images, but now also incorporates imagery from Landsat 7 and 8, CBERS-2, CBERS-2B, Resourcesat-1, and UK2-DMC. PRODES is operated by the National Institute of Space Research (INPE) in collaboration with the Ministry of the Environment (MMA) and the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). Since 2002, all PRODES data is publicly available online.

Input images for each of the 220 Landsat footprints that cover the Brazilian Amazon are selected based on their lack of cloud cover and their capture date. The PRODES system uses the seasonal year, starting on August 1st, to calculate annual deforestation, so images are selected as near to this date as possible (generally from July, August, and September). From 2003 to 2005, analysts used image transformation to determine the components of vegetation, soil, and shadow using the program SPRING. These components were segmented and classified into the classes of forest, non-forest, deforestation in the target year, previous deforestation, clouds, and water, which are then manually corrected by experts. Starting in 2005, a new methodology was implemented which makes use of the open source TerraAmazon platform. The platform allows the PRODES analysis to be more uniform and can incorporate imagery from a variety of satellites. As before, images are selected to be as cloud free as possible. The images are then masked to exclude non-forest, previous deforestation, and water using the previous year’s analysis. Analysts then delineate deforested polygons in the intact forest of the previous year. More information on the methodology can be found on the PRODES website.

For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities. “Loss” indicates the removal or mortality of tree canopy cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.

When zoomed out (< zoom level 13), pixels of gain are shaded according to the density of gain at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover gain, whereas pixels with lighter shading indicate a lower concentration of tree cover gain. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).

A validation assessment of the 2000 – 2012 Hansen/UMD/Google/USGS/NASA change data was carried out independently from the mapping exercise at the global and biome (tropical, subtropical, temperate, and boreal) scale. A stratified random sample (for no change, loss, and gain) of 1500 blocks, each 120 × 120 meters, was used as validation data. The amount of tree cover gain within each block was estimated using Landsat, MODIS, and Google Earth high-resolution imagery and compared to the map. Overall accuracies for gain were over 99.5% globally and for all biomes. However, since the overall accuracy calculations are positively skewed due to the high percentage of no change pixels, it is also important to assess the accuracy of the change predictions. The user’s accuracy (i.e. the percentage of pixels labelled as tree cover gain that actually gained tree cover) was 87.8% at the global level. At the biome level, user’s accuracies were 81.9%, 85.5%, 62.0%, and 76.7% for the tropical, subtropical, temperate, and boreal biomes, respectively.

Overview

This data set measures areas of tree cover gain across all global land (except Antarctica and other Arctic islands) at 30 × 30 meter resolution, displayed as a 12-year cumulative layer. The data were generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. Over 600,000 Landsat 7 images were compiled and analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis. The clear land surface observations (30 × 30 meter pixels) in the satellite images were assembled and a supervised learning algorithm was then applied to identify per pixel tree cover gain.

Tree cover gain was defined as the establishment of tree canopy at the Landsat pixel scale in an area that previously had no tree cover. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop rotation cycle of tree plantations.

Varies according to selection (use the legend on the map to change the minimum tree cover canopy density threshold)

Cautions

This data layer was updated in January 2015 to extend the tree cover loss analysis to 2013, and in August 2015 to extend the tree cover loss analysis to 2014. The updates include new data for the target year and re-processed data for the previous two years (2011 and 2012 for the 2013 update, 2012 and 2013 for the 2014 update). The re-processing increased the amount of change that could be detected, resulting in some changes in calculated tree cover loss for 2011-2013 compared to the previous versions. Calculated tree cover loss for 2001-2010 remains unchanged. The integrated use of the original 2001-2012 (Version 1.0) data and the updated 2011–2014 (Version 1.1) data should be performed with caution.

For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities. “Loss” indicates the removal or mortality of tree canopy cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.

When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).

Overview

This data set measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and approximately 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2014 interval. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss.

Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced.

2015 Update (Version 1.1)

This data set has been updated twice since its creation, and now includes loss up to 2014. The analysis method has been modified in numerous ways, and the update should be seen as part of a transition to a future “version 2.0” of this data set that is more consistent over the entire 2001 and onward period. Key changes include:

The use of Landsat 8 data for 2013-2014 and Landsat 5 data for 2011-2012

The reprocessing of data from the previous two years in measuring loss (2011 and 2012 for the 2013 update, 2012 and 2013 for the 2014 update)

Improved training data for calibrating the loss model

Improved per sensor quality assessment models to filter input data

Improved input spectral features for building and applying the loss model

These changes lead to a different and improved detection of global tree cover loss. However, the years preceding 2011 have not yet been reprocessed with the revised analysis methods, and users will notice inconsistencies between versions 1.0 (2001-2012) and 1.1 (2011-2014) as a result. It must also be noted that a full validation of the results incorporating Landsat 8 has not been undertaken. Such an analysis may reveal a more sensitive ability to detect and map forest disturbance using Landsat 8 data. If this is the case then there will be a more fundamental limitation to the consistency of this data set before and after the inclusion of Landsat 8 data. Validation of Landsat 8-incorporated loss detection is planned.

Some examples of improved change detection in the 2011–2014 update include the following:

These are examples of dynamics that may be differentially mapped over the 2011-2014 period in Version 1.1. A version 2.0 reprocessing of the 2001 and onward record is planned, but no delivery date is yet confirmed.

While Landsat 7 and 8 satellites together have a revisit period of 8 days, cloud cover can majorly limit the availability of imagery, particularly in the wet season. Alert dates represent the instance of detection, though tree cover loss could have taken place weeks earlier due to persistent cloud cover.

In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height with greater than 60% canopy cover, and may take the form of natural forests or plantations. “Loss” indicates the removal or mortality of >=50% of tree cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.

In Peru, where the alert system was first prototyped, the authors evaluated the data to have 13.5% commission error (false positives), though the value drops to 1% when excluding pixels on the boundaries of other loss and unconfirmed alerts. The data has 33% omission errors, though most of these occur in secondary forests where tree cover and other vegetation are more difficult to distinguish. Omission errors in primary forests fall to 17% when excluding pixels on the boundaries of other loss, suggesting that most of the omissions happen on the edges of loss patches.

This data set, created by the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland and supported by Global Forest Watch, is the first Landsat-based alert system for tree cover loss. While most existing loss alert products use 250-meter resolution MODIS imagery, these alerts have a 30-meter resolution and thus can detect loss at a much finer spatial scale. The alerts are currently operational for Peru, the Republic of Congo, and Kalimantan in Indonesia, and will eventually be expanded to the rest of the humid tropics.

New Landsat 7 and 8 images are downloaded as they are posted online at USGS EROS, assessed for cloud cover or poor data quality, and compared to the three previous years of Landsat-derived metrics (including ranks, means, and regressions of red, infrared and shortwave bands, and ranks of NDVI, NBR, and NDWI). The metrics and the latest Landsat image are run through seven decision trees to calculate a median probability of forest disturbance. Pixels with probability >50% are reported as tree cover loss alerts. Alerts remain unconfirmed until two or more out of four consecutive observations are labelled as tree cover loss. For more information on methodology, see the forthcoming Environmental Research Letters paper.

While Landsat 7 and 8 satellites together have a revisit period of 8 days, cloud cover can majorly limit the availability of imagery, particularly in the wet season. Alert dates represent the instance of detection, though tree cover loss could have taken place weeks earlier due to persistent cloud cover.

In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height with greater than 60% canopy cover, and may take the form of natural forests or plantations. “Loss” indicates the removal or mortality of >=50% of tree cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.

In Peru, where the alert system was first prototyped, the authors evaluated the data to have 13.5% commission error (false positives), though the value drops to 1% when excluding pixels on the boundaries of other loss and unconfirmed alerts. The data has 33% omission errors, though most of these occur in secondary forests where tree cover and other vegetation are more difficult to distinguish. Omission errors in primary forests fall to 17% when excluding pixels on the boundaries of other loss, suggesting that most of the omissions happen on the edges of loss patches.

This data set, created by the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland and supported by Global Forest Watch, is the first Landsat-based alert system for tree cover loss. While most existing loss alert products use 250-meter resolution MODIS imagery, these alerts have a 30-meter resolution and thus can detect loss at a much finer spatial scale. The alerts are currently operational for Peru, the Republic of Congo, and Kalimantan in Indonesia, and will eventually be expanded to the rest of the humid tropics.

New Landsat 7 and 8 images are downloaded as they are posted online at USGS EROS, assessed for cloud cover or poor data quality, and compared to the three previous years of Landsat-derived metrics (including ranks, means, and regressions of red, infrared and shortwave bands, and ranks of NDVI, NBR, and NDWI). The metrics and the latest Landsat image are run through seven decision trees to calculate a median probability of forest disturbance. Pixels with probability >50% are reported as tree cover loss alerts. Alerts remain unconfirmed until two or more out of four consecutive observations are labelled as tree cover loss. For more information on methodology, see the forthcoming Environmental Research Letters paper.

Alerts become confirmed when a second satellite pass has also identified the pixel as an alert. Most of the alerts that are not confirmed have not had another satellite pass, due to the 8-day revisit time or cloud cover.

2015 DATA COVERAGE

This layer shows the areas of the world that have updated tree cover loss data for 2015, including Latin America, Africa, southeast Asia, and Oceania. Other areas of the world currently only have data available until 2014, but will be updated to include 2015 tree cover loss in the coming months.

The data does not distinguish between primary and regenerated forests. Regenerated areas that appear as forests in the previous two years and experience change will be marked as deforestation.

Plots are identified as deforestation in the month when they are first identified. If imagery for previous months were poor quality or covered by clouds, the actual deforestation event may have happened before the reported month.

Overview

The tropical dry forests of the Gran Chaco region in Paraguay, Argentina, and Bolivia have become a hotspot of deforestation as cattle ranching and soy expand into the area. Deforestation monitoring in the Gran Chaco has been carried out by the non-profit Guyra Paraguay since 2011, using 30-meter resolution Landsat images for the 55 scenes that cover the Gran Chaco. The interpretation of forest change areas is done through multi-temporal analysis, which uses a base image from the last two years and a current image from the study month. Analysts use visual interpretation techniques to identify deforestation, including elements of tone, shape, size, texture, pattern, shadow, and context.

Viewing data for the first period available via the time slider may include alerts that reflect detections from the February 2000 to December 2005 training period as the algorithm "catches up" post-training.

The GFW team has clipped out some data in regions where data quality is suspected to be low because of persistent cloud cover over identified ecoregions. This covers parts of Liberia, Venezuela, Guyana, Vietnam, Laos, and Burma/Myanmar. The current extent of the FORMA alerts is available for viewing here—FORMA geographic extent.

The algorithm behind the FORMA alerts is constantly evolving to fix bugs and improve accuracy. As a result, what appears on the site and the results of analyses conducted on the site may change over time. It is important to always include the access date when citing FORMA. Analysis of FORMA alerts on the map should be considered definitive, as analysis is based on raw FORMA data, whereas the data on the map are optimized for visual display. For questions, please join the GFW discussion forum or email us.

For the purpose of this study, “tree cover” was defined as areas with greater than 25% canopy cover (as determined by the Vegetation Continuous Fields data set), and change was measured without regard to forest land use. Tree cover assemblages that meet the 25% threshold include intact forests, plantations, and forest regrowth.

When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.

Overview

FORMA is a near real-time tree cover loss alert system. It uses a cloud computing algorithm to analyze frequently updated satellite imagery along with complementary information on factors that affect tree cover loss, such as fires and precipitation. The system generates twice-monthly “alerts” for the world’s humid tropical forests that identify 500 × 500 meter areas where new, large-scale loss is likely to have occurred.

FORMA is designed for quick identification of new areas of tree cover loss. The system analyzes data gathered daily by the MODIS sensor, which operates on NASA’s Terra and Aqua satellites. The FORMA alerts system then detects pronounced changes in vegetation cover over time, as measured by the Normalized Difference Vegetation Index (NDVI), a measure of vegetation greenness. These pronounced changes in vegetation cover are likely to indicate forest being cleared, burned, or defoliated.

FORMA alerts only appear in areas where the probability of tree cover loss is greater than or equal to 50%.

Upcoming upgrades to FORMA include improving the resolution to 250 × 250 meters, and expanding coverage to tropical dry forest and eventually to other biomes across the global scale.

Methodology

This data set uses freely available satellite imagery, collected by the Moderate Resolution Imaging Spectroradiometer (MODIS), which operates on NASA's Terra and Aqua (EOS PM) satellite platforms and views the entire Earth’s surface every 1 to 2 days. The images help to reveal notable changes in vegetation cover over time using an indicator that measures vegetation intensity called the Normalized Difference Vegetation Index (NDVI). By applying an automated algorithm to this input and in combination with complementary data inputs for fire hotspots and global precipitation averages, areas where tree cover loss is likely to have occurred are therefore identified. The algorithm also employs parallel processing in a remote server system the cloud that enables the rapid analysis of these very large data sets.

Explanation

Tree cover loss alerts appear when and where new, large-scale loss is likely to have occurred after 2005. Thus the alerts should not be interpreted as an analysis of total tree cover loss area but rather as an indication of an area that has a high probability of having experienced tree cover loss or disturbance over time. The system employs advanced statistical techniques to achieve the best fit to scientifically validate information on loss, measured as a probability. On the GFW website, alerts appear only for areas where there is a 50% or higher probability of tree cover loss. That is, alerts appear for a particular period when there has been significant loss in the area during or before that period.

Temporal and spatial resolution

FORMA alerts are displayed on the GFW site as monthly data, and the site is updated each month. Users who download data for analysis will find that the underlying data set is actually available at 16-day intervals, intervals that do not line up perfectly with calendar months. Data is displayed at a monthly resolution on GFW due to the monthly availability of precipitation data input. Users can manipulate the GFW time slider to view trends in loss alerts from December 2005 to present.

The alert system currently identifies 500 × 500 meter areas where loss is statistically likely to have occurred. Alerts for a 250-meter resolution version are currently being developed on Google’s Earth Engine platform and will be available in 2014.

Geographic extent

FORMA alerts are currently available only for humid tropical forests (as defined by Hansen et al. (2008), based on WWF’s terrestrial ecoregions) spanning portions of 89 countries. The development team is working to incorporate additional data to extend the geographic coverage beyond the current extent. To visualize the geographic extent of the alerts on the map, switch on the “Humid Tropical Forest Biome” layer.

Data applications

The alerts have been designed for quick identification of tree cover loss as it happens. This allows for rapid response and prioritization of scarce financial and human resources dedicated to forest conservation or sustainable forest management. Armed with this information, stakeholders can use preemptive methods such as on-the-ground visits or aerial inspection with high-resolution satellite imagery (less than 5-meter pixel resolution) to investigate suspected tree cover loss areas.

In addition, the alerts may be of value to a variety of researchers who study both temporal and spatial patterns related to tree cover loss areas.

Using the GFW platform, the alerts can be compared against other relevant data layers, such as protected areas and concessions boundaries, to evaluate the effectiveness of forest management practices across time and spatial extent.

Accuracy and validation

Inaccuracies are an inherent part of remote sensing analysis. FORMA alerts appear in areas with a greater than 50% probability of tree cover loss, based on the algorithm described under Methodology. However, persistent cloud cover is a continuous issue in the tropics, and extreme flooding can also produce unreliable remotely sensed data that will result in tree cover loss “false positives” (alerts where no actual tree cover loss has occurred). Furthermore, the alerting system cannot detect all forest cover loss, whether due to the small size of the loss area, persistent cloud cover, or other explanations still being identified through GFW validation efforts.

The major instances of false positives may occur as the following:

A random, "speckled" distribution of alerts across an ecoregion, or complete filling of a small ecoregion. Caused by limited or sparse training data, particularly in small ecoregions, which makes it difficult to tune the model there. As a result, alerts cannot be reliably detected. In a normal ecoregion, alerts are usually clustered.

A rapid explosion of alerts over 1-3 months covering a relatively large area. Caused by a significant, persistent drop in detected vegetation levels due to persistent cloud cover along coastlines, in mountains, or elsewhere.

Alerts in water. Caused by shifting water bodies. These alerts should be considered not necessarily as false positives but rather as ambiguous alerts requiring additional data for corroboration.

The GFW team is working aggressively to address potential inaccuracies in the data through rigorous validation methods. Specifically, the GFW team is comparing the growing data set of historical alerts to other validated data sets, which are being used for similar applications.

This issue brief demonstrates the spatial correlation of the alerts with the PRODES and DETER data sets, produced by the Brazilian Space Agency for the Amazon. The conclusions from the working paper help to illustrate the potential pitfalls of the algorithm, along with its strengths. Through future refinement and proposed crowdsourcing efforts, the GFW team expects the data quality of the FORMA Alerts will continue to improve.

Geographic coverage of FORMA alerts

This data layer shows the geographic coverage of FORMA alerts, which largely corresponds to the extent of the humid tropical forest biome, as defined by Hansen et al. (2008), and based on WWF’s terrestrial ecoregions. The biome illustrated by this layer includes a number of smaller forest ecoregions, which span portions of 89 countries.

This data layer shows the geographic coverage of FORMA alerts, which largely corresponds to the extent of the humid tropical forest biome, as defined by Hansen et al. (2008), and based on WWF’s terrestrial ecoregions. The biome illustrated by this layer includes a number of smaller forest ecoregions, which span portions of 89 countries.

When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.

Overview

The Deforestation Alert System (Sistema de Alerta de Desmatamento—SAD) is a monthly alert that monitors forest cover loss and forest degradation in the Brazilian Amazon. The system generates information that is published monthly by Imazon, a Brazilian NGO, through its Forest Transparency Bulletin. The monthly alerts are derived from a temporal mosaic of MODIS daily images that are scaled down from 500 × 500 meter to 250 × 250 meter resolution. The monthly results are then validated using medium resolution images from the China-Brazil Earth Resources Satellite (CBERS) and NASA Landsat data in order to “ground-truth” the results being reported.

Citation: Souza, C. M., S. Hayashi, and A. Veríssimo. 2009. “Near Real-Time Deforestation Detection for Enforcement of Forest Reserves in Mato Grosso.” FIG—Land Governance in Support of the MDGS: Responding to New Challenges.www.fig.net/pub/fig_wb_2009/papers/trn/trn_2_souza.pdf.

Not all fires are detected. There are several reasons why MODIS may not have detected a certain fire. The fire may have started and ended between satellite overpasses. The fire may have been too small or too cool to be detected in the (approximately) 1 km2 pixel. Cloud cover, heavy smoke, or tree canopy may completely obscure a fire.

It is not recommended to use active fire locations to estimate burned area due to spatial and temporal sampling issues.

When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.

Overview

The Fire Information for Resource Management System (FIRMS) delivers global MODIS-derived hotspots and fire locations. The active fire locations represent the center of a 1-kilometer pixel that is flagged by the MOD14/MYD14 Fire and Thermal Anomalies Algorithm as containing one or more fires within the pixel.

Given the lack of ground-based data, the methodology was validated using data from other forest monitoring systems such as PRODES (http://www.obt.inpe.br/prodes/index.php) which have been validated separately.

All clouds, water, and mist were masked based on MODIS Quality Assessment and MOD35 products and their values changed to “No Data”.

The Terra-i algorithm for change detection does not automatically identify events that occurred because of wild fires or within secondary forests or oil palm plantations. Furthermore, the moderate resolution of the MODIS sensor does not detect small scale events (<5ha). Terra-i is intended to be used to quickly identify deforestation hotspots which should then be more thoroughly investigated with higher resolution imagery or field validation.

Overview

Terra-i is a near real-time monitoring system that detects land cover changes in Latin America. It uses satellite data from MODIS vegetation indices (MOD13Q1 and NDVI) and products related to presence of water bodies (MOD35) as well as Tropical Rainfall Measuring Mission (TRMM) precipitation data to detect anthropogenic changes in vegetation cover every 16 days. Terra-i is a collaboration between the International Center for Tropical Agriculture (CIAT - DAPA), CGIAR’s Research Program on Forestry, Trees and Agroforestry (FTA), The Nature Conservancy (TNC), the University of Applied Sciences Western Switzerland (HEIG-VD), and King’s College London (KCL).

The system, which uses a computational algorithm similar to FORMA (http://www.globalforestwatch.org/sources/forest_change#forma), is based on the premise that natural vegetation follows a predictable pattern of change in greenness from one date to the next, brought about by site-specific land and climatic conditions over the same period. The model is trained to understand the normal pattern of changes in vegetation greenness in relation to terrain and rainfall for a site, which allows for prediction of what the next vegetation response should be based on the historical data. If the prediction is significantly different from the historical responses in relation to pattern of rainfall and lasts for two 16-day periods in a row, the pixel is marked as potentially having changed by anthropogenic means.

Varies according to selection (click the gear icon on the map to change the minimum tree cover canopy density threshold)

Cautions

For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities.

Overview

This data set displays tree cover over all global land (except for Antarctica and a number of Arctic islands) for the year 2000 at 30 × 30 meter resolution. “Percent tree cover” is defined as the density of tree canopy coverage of the land surface and is color-coded by density bracket (see legend).

Data in this layer were generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis, to determine per pixel tree cover using a supervised learning algorithm.

Shows carbon density values of aboveground live woody biomass across the tropics.

RESOLUTION / SCALE

30 m.

Geographic coverage

Tropics

Source data

ICEsat GLAS lidar, MODIS, Landsat, ground measurements.

Date of content

2000

Tree cover canopy density

Varies according to selection (use the legend on the map to change the minimum tree cover canopy density threshold)

Cautions

It is recommended that both aboveground carbon density and uncertainty values be used together for carbon assessments and verification. The map will provide accurate estimates of aboveground carbon stock and aboveground carbon density when aggregated to large areas (5,000 to 10,000 ha) for project and regional level assessments. The biomass density value of a single pixel may have large uncertainty when compared with small plots for verification.

License

Creative Commons CC BY 4.0

Overview

This is a higher resolution data product that expands upon the methodology presented in Baccini et al. (2012) to generate a pan-tropical map of aboveground live woody biomass density at 30 m resolution for circa the year 2000. Along with the carbon density values, there is an error map at the same spatial resolution providing the uncertainty in aboveground carbon density estimation. These maps allow for the co-location of biomass estimates with Hansen et al. (2013, v1.0) tree cover loss estimates at similar spatial resolution. The statistical relationship derived between ground-based measurements of forest biomass density and co-located Geoscience Laser Altimeter System (GLAS) LiDAR waveform metrics as described by Baccini et al. (2012) were used to estimate the biomass density of more than 40,000 GLAS footprints throughout the tropics. Then, using randomForest models, the GLAS-derived estimates of biomass density were correlated to continuous, gridded variables including Landsat 7 ETM+ satellite imagery and products (e.g., reflectance), elevation, and biophysical variables. By using continuous gridded datasets as inputs to the randomForest models, a wall-to-wall 30 m resolution map of aboveground woody biomass density across the tropics was produced as well as the associated uncertainty layer. The uncertainty layer takes into account the errors from allometric equations, LiDAR based model, and randomForest model. All the errors are propagated to the final biomass estimate. A detailed description of the work will be reported in a new paper under preparation.

Identifies the world’s last remaining unfragmented forest landscapes, large enough to retain all native biodiversity and showing no signs of human alteration as of the year 2013. This layer also shows the reduction in the extent of Intact Forest Lanscapes from 2000 to 2013.

The world IFL map was created through visual interpretation of Landsat images by experts. The map may contain inaccuracies due to limitations in the spatial resolution of the imagery and lack of ancillary information about local land-use practices in some regions. In addition, the methodology assumes that fires in proximity to roads or other infrastructure may have been caused by humans, and therefore constitute a form of anthropogenic disturbance. This assumption could result in an underestimation of IFL extent in the boreal biome.

Overview

The Intact Forest Landscapes (IFL) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. To map IFL areas, a set of criteria was developed and designed to be globally applicable and easily replicable, the latter to allow for repeated assessments over time as well as verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. These were then mapped from Landsat imagery for the years 2000 and 2013.

Changes in the extent of IFLs were identified within year 2000 IFL boundary using the global wall-to-wall Landsat image composite for year 2013 and the global forest cover loss dataset (Hansen et al., 2013). Areas identified as “reduction in extent” met the IFL criteria in 2000, but no longer met the criteria in 2013. The main causes of change were clearing for agriculture and tree plantations, industrial activity such as logging and mining, fragmentation due to infrastructure and new roads, and fires assumed to be caused by humans.

This data can be used to assess forest intactness, alteration, and degradation at global and regional scales. More information about the data set and methodology is available on www.intactforests.org

Identifies the world’s last remaining undisturbed forest areas, large enough to retain all native biodiversity and showing no signs of human activity as of the year 2013 and reduction in their extent from 2000-2013.

The world IFL map was created through visual interpretation of Landsat images by experts. The map may contain inaccuracies due to limitations in the spatial resolution of the imagery and lack of ancillary information about local land-use practices in some regions. In addition, the methodology assumes that fire scars in proximity to roads or other infrastructure have been caused by humans, and therefore constitute a form of significant human activity. This assumption could result in an underestimation of IFL extent in the boreal ecozone. The attribution of forest fires to human influence across boreal forest landscapes is disputed.

Overview

The Intact Forest Landscapes (IFL) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. To map IFL areas, a set of criteria was developed and designed to be globally applicable and easily replicable, the latter to allow for repeated assessments over time as well for verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. These were then mapped from Landsat imagery for the years 2000 and 2013.

Changes in the extent of IFLs were identified by contrasting the map for 2013 with the analogous map for 2000, adjusted for consistency. Areas identified as “reduction in extent” met the IFL criteria in 2000, but no longer met the criteria in 2013. The main causes of change were industrial activity such as logging and mining, and fragmentation due to infrastructure and new roads, and fires assumed to be caused by humans.

This data can be used to assess forest intactness, alteration, and degradation at global and regional scales. More information about the data set and methodology is available on www.intactforests.org

Suggested citations for data as displayed on GFW: Greenpeace, University of Maryland, World Resources Institute and Transparent World. 2014. Intact Forest Landscapes: update and reduction in extent from 2000-2013. Accessed through Global Forest Watch on [date]. www.globalforestwatch.org

Observatoire Satellital des forêts d'Afrique centrale (OSFAC), South Dakota State University (SDSU), and University of Maryland (UMD)

Date of content

2000

Cautions

The accuracy of this data has not been assessed

Overview

This data set shows the coverage of primary humid tropical forest in the Democratic Republic of the Congo in the year 2000 at a 60 meter resolution. “Primary forest” is defined in this data set as mature humid tropical forest with greater than 60% canopy cover, and differs from “secondary forest” (regrowing forest with greater than 60% canopy cover) and “woodlands” (between 30% and 60% canopy cover). The authors created a composite of cloud-free Landsat imagery during the growing season of 2000 to conduct the analysis. They applied supervised bagged classification tree models to separate forest areas from non-forest based on training sites. Within forest areas, primary forests were separated from secondary forests and woodlands using supervised classification. For more information on methodology, see here.

Citation: Observatoire Satellital des forêts d'Afrique centrale, South Dakota State University, and University of Maryland. “Democratic Republic of the Congo primary forests”. Accessed through Global Forest Watch on [date]. www.globalforestwatch.org

This layer represents a draft visualization of the data. Artifacts such as white vertical lines, or visually degraded pixels may occur temporarily. Also note that definition of primary forests used to create this layer may not match other definitions of primary forests. See the overview section for details.

Overview

This data set indicates the location of intact and degraded primary forests across Indonesia as of the year 2000. Primary forest consist of mature natural forest cover that has not been completely cleared in recent history (30 years or more) and consisted of a contiguous block of 5 ha or more. Primary forest cover was mapped using Landsat composites and multi-temporal metrics as input data to a two-step supervised classification. The first step was a per-pixel classification of areas with tree canopy cover of 30% and above for the 2000 reference year.

A second per-pixel classification procedure was performed to separate primary forest from other tree cover for 2000; contiguous areas of 5 ha and greater were retained as primary forest. A limited editing of this classification was performed to remove older plantations and adjust other forest formations that could not be identified using the per-pixel classifier, but could be identified in photo-interpretive contexts. Primary forests were subsequently characterized into primary intact and primary degraded subclasses using the GIS-based buffering approach of the Intact Forest Landscapes (IFL). To create the IFL layer, buffers of roads, settlements and other signs of human landscape alteration were used to identify degraded areas within zones of primary forest cover. IFL mapping employed cloud-free Landsat mosaics to quantify changes in primary intact forest extent. The map of primary intact and primary degraded forest cover types corresponds to the Indonesia Ministry of Forestry’s primary and secondary forest cover types.

This data set shows the global distribution of mangrove forests, derived from earth observation satellite imagery

RESOLUTION / SCALE

Mapped from 30m Landsat imagery

Geographic coverage

Global

Source data

Landsat Global Land Survey Collection

Frecuency of updates

None planned

Date of content

1997-2000

Cautions

Results were validated using existing distribution data and published literature.

Note that small patches (< 900-2,700 sq-m) of mangrove forests cannot be identified using this approach. This methodological approach had a number of challenges, such as cloud cover and noise. There may also be areas where land cover was misclassified.

As the data set may still contain overlapping polygons, a dissolve operation (within a GIS) might be needed before surface area calculations are carried out.

Overview

To improve scientific understanding of the extent and distribution of mangrove forests of the world the status and distribution of global mangroves were mapped using recently available Global Land Survey (GLS) data and the Landsat archive.The project interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Results were validated using existing GIS data and the published literature to map ‘true mangroves’.

The total area of mangroves in the year 2000 was 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude.

The remaining area of mangrove forest in the world is less than previously thought; the estimate provided in this study is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. This data set presents the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created

This data set contains all of the data that is currently available to Open Development Cambodia (ODC) and is not exhaustive. Projects or areas with publicly known boundaries are mapped as polygons; those without publicly known boundaries are represented as dots on the map. The dots are placed in the center of the closest known geographical area and thus do not represent exact locations. The development landscape is constantly changing, and there are also additional developments for which data is not available. While ODC takes every effort to ensure that the details in this map are accurate and up to date, some of the projects or areas marked on the map may have since been modified or cancelled since the map was published. Moreover, additional developments may have been approved that are not yet included here.

Note: Publicly available information on land area reductions from ELCs does not include maps or spatial data of excisions. Thus, ODC cannot present land area cut in shapes. As a result, ELC projects that are visualized on the interactive map represent the original size.

Overview

Economic Land Concessions (ELC) are long-term leases that allows a concessionaire to clear land in order to develop industrial-scale agriculture. In recent years, communities, local and international organizations, UN agencies, and development partners have raised many concerns about the impact of ELCs on communities and the environment. Both local and international media frequently report on cases of communities losing land to concession holders. Further, ELCs allegedly have led to deforestation as large tracts of forestland are cleared for plantations.

This dataset contains data for ELCs in Cambodia with contract dates starting from 1995 to 2012. The list was updated in June 2015 to included adjustments to ELCs contracts as a result of the Directive 01, starting in 2012, such as land cuts and cancellation of licenses. Due to the lack of publicly available information, this dataset does not include information on reductions of contract durations as a result of the ELCs evaluation process in 2015.

Open Development Cambodia collected the data from a variety of public domain sources such as the government, non-governmental organizations (NGOs), research institutes, company websites, and news reports.

Citation: Open Development Cambodia. “Economic Land Concessions.” Accessed through Global Forest Watch on [date].www.globalforestwatch.org

Depicts the spatial distribution of ownership types across forest land in the USA.

Resolution / Scale

250 × 250 meters

Geographic coverage

Conterminous United States

Source

Forest land was identified using a forest probability dataset (Wilson et al. 2012). Public and private ownership types were derived from Forest Inventory and Analysis (FIA) plot data, with federal and state land ownership supplemented with data from the Protected Areas Database of the United States (CBI 2012).

Frequency of updates

Irregular

Date of content

2009

Cautions

The data are designed for strategic analysis at a national or regional scale; it is not recommended to use the data for tactical analyses on a sub-regional scale, or for informing local management decisions. Furthermore, map accuracies vary considerably and thus the utility of the data can vary geographically under different ownership patterns.

License

Public data set

Overview

This data product contains raster data depicting the spatial distribution of forest ownership types in the conterminous United States circa 2009. The data are a modeled representation of forest land by ownership type, and include three types of public ownership: federal, state, and local, as well as three types of private ownership: family (includes individuals and families), corporate, and other private (includes conservation and natural resource organizations, unincorporated partnerships and associations, and Native American tribal lands).

NLCD land cover products have been published for 2001, 2006, and 2011. A formal accuracy assessment has not been conducted for NLCD 2011 Land Cover.

An assessment of accuracy for the NLCD land cover product found overall accuracies for the 2001 and 2006 products were 79% and 78%, respectively, with accuracies exceeding 80% for water, high density urban, all upland forest classes, shrubland, and cropland for both dates.

Overview

The National Land Cover Database 2011 (NLCD 2011) is the most recent national data product created by the United States Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. NLCD 2011 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2011. As with two previous NLCD land cover products NLCD 2011 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2011 is based primarily on a decision-tree classification of circa 2011 Landsat satellite data.

The 2011 land cover layer is one of five primary data products produced as part of the NLCD 2011: 1) NLCD 2011 Land Cover 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class 3) NLCD 2011 Percent Developed Imperviousness 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels 5) NLCD 2011 Tree Canopy Cover.

Land cover class categories include forest, planted/cultivated lands, wetland, grassland, water, developed areas and barren land. Land cover information is critical for local, state, and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics, and developing land management policies.

NLCD land cover products have been published for 2001, 2006, and 2011, with change products available for 2001 to 2006, 2006 to 2011, and 2001 to 2011. This layer contains less overall change than the sum of 2001/2006 and 2006/2011 land cover change pixels as some transitioned through two classes from 2001 to 2006 to 2011. In this case, the latest change class is given.

An assessment of accuracy for the NLCD land cover product found overall accuracies for the 2001 and 2006 products were 79% and 78%, respectively, with accuracies exceeding 80% for water, high density urban, all upland forest classes, shrubland, and cropland for both dates.

Overview

This layer displays change in US land cover between 2001 and 2011. Pixels that changed during this period display the land cover value that they changed to. Pixels with no change are transparent.

The National Land Cover Database 2011 (NLCD 2011) is the most recent national data product created by the United States Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. NLCD 2011 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2011. As with two previous NLCD land cover products NLCD 2011 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2011 is based primarily on a decision-tree classification of circa 2011 Landsat satellite data.

The 2001/2011 land cover change layer is one of five primary data products produced as part of the NLCD 2011: 1) NLCD 2011 Land Cover 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class 3) NLCD 2011 Percent Developed Imperviousness 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels 5) NLCD 2011 Tree Canopy Cover.

Land cover class categories include forest, planted/cultivated lands, wetland, grassland, water, developed areas and barren land. Land cover information is critical for local, state, and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics, and developing land management policies

Data gaps in the MERIS 2009 satellite acquisitions (about 5% of the total data area) were filled using the GlobCover 2005 land cover map.

The lack of a short-wave infrared (SWIR) band in the MERIS sensor may result in underestimation of flooded forests. These classes were therefore imported from the 2005 GlobCover land cover map.

Delineation between land and water is not exhaustive, especially for inland water bodies, and locations of water may be inaccurate.

Overview

GlobCover is a European Space Agency (ESA) initiative which began in 2005 in partnership with the Joint Research Center, European Environmental Agency, UN Food and Agricultural Organization, UN Environment Programme, Global Observation of Forest Cover and Land Cover Dynamics, and International Geosphere-Biosphere Programme. The aim of the project was to develop a service capable of delivering global composites and land cover maps using observations from the 300 meter MERIS sensor on board the ENVISAT satellite mission. ESA makes land cover maps available covering 2 periods: December 2004 - June 2006 and January - December 2009. GlobCover products come with a thematic legend compatible with the UN Land Cover Classification System (LCCS).

Data in this layer was generated using MERIS images which were classified using both a supervised (human-verified) and unsupervised (automated)classification algorithm applied at two different seasonal time steps to create land cover classes based on both spectral and temporal properties of land cover. The labelling procedure is automated and based on the GlobCover 2005 (V2.2) land cover map. Several decision rules have been defined with the help of international land cover experts to create unique labels for each class.

Overview

This data set was created by Transparent World, with the support of Global Forest Watch. Many studies depicting forest cover and forest change cannot distinguish between natural forests and plantations. This data set attempts to distinguish tree plantations from natural forest for seven key countries: Brazil, Cambodia, Colombia, Indonesia, Liberia, Malaysia, and Peru.

Given the variability of plantations and their spectral similarity to natural forests, this study used visual interpretations of satellite imagery, primarily Landsat, supplemented by high resolution imagery (Google Maps, Bing Maps, or Digital Globe), where available, to locate plantations. Analysts hand-digitized plantation boundaries based on several key visual criteria, including texture, shape, color, and size.

Each polygon is labelled with the plantation type and when possible, the species. A “gr” in front of the species name indicates a group of species, such as pines or fruit, where the individual species was not identifiable. The percentage of plantation coverage indicates a rough estimate of the prevalence of plantation within apolygon (as in the case of a mosaic). Types are defined as follows:

Large industrial plantation: single plantation units larger than 100 hectares

Mosaic of medium-sized plantations: mosaic of plantation units < 100 hectares embedded within patches of other land use

Mosaic of small-sized plantations: mosaic of plantation units < 10 hectares embedded within patches of other land use.

Clearing/ very young plantation: bare ground with contextual clues suggesting it will become a plantations (shape or pattern of clearing, proximity to other plantations, distinctive road network, etc)

For more information on this data set and how it was produced, see the forthcoming WRI Technical Note associated with this project.

Overview

This data set was created by Transparent World, with the support of Global Forest Watch. Many studies depicting forest cover and forest change cannot distinguish between natural forests and plantations. This data set attempts to distinguish tree plantations from natural forest for seven key countries: Brazil, Cambodia, Colombia, Indonesia, Liberia, Malaysia, and Peru.

Given the variability of plantations and their spectral similarity to natural forests, this study used visual interpretations of satellite imagery, primarily Landsat, supplemented by high resolution imagery (Google Maps, Bing Maps, or Digital Globe), where available, to locate plantations. Analysts hand-digitized plantation boundaries based on several key visual criteria, including texture, shape, color, and size.

Each polygon is labelled with the plantation type and when possible, the species. A “gr” in front of the species name indicates a group of species, such as pines or fruit, where the individual species was not identifiable. The percentage of plantation coverage indicates a rough estimate of the prevalence of plantation within apolygon (as in the case of a mosaic). Types are defined as follows:

Large industrial plantation: single plantation units larger than 100 hectares

Mosaic of medium-sized plantations: mosaic of plantation units < 100 hectares embedded within patches of other land use

Mosaic of small-sized plantations: mosaic of plantation units < 10 hectares embedded within patches of other land use.

Clearing/ very young plantation: bare ground with contextual clues suggesting it will become a plantations (shape or pattern of clearing, proximity to other plantations, distinctive road network, etc)

For more information on this data set and how it was produced, see the forthcoming WRI Technical Note associated with this project.

Overview

This data set was created by Transparent World, with the support of Global Forest Watch. Many studies depicting forest cover and forest change cannot distinguish between natural forests and plantations. This data set attempts to distinguish tree plantations from natural forest for seven key countries: Brazil, Cambodia, Colombia, Indonesia, Liberia, Malaysia, and Peru.

Given the variability of plantations and their spectral similarity to natural forests, this study used visual interpretations of satellite imagery, primarily Landsat, supplemented by high resolution imagery (Google Maps, Bing Maps, or Digital Globe), where available, to locate plantations. Analysts hand-digitized plantation boundaries based on several key visual criteria, including texture, shape, color, and size.

Each polygon is labelled with the plantation type and when possible, the species. A “gr” in front of the species name indicates a group of species, such as pines or fruit, where the individual species was not identifiable. The percentage of plantation coverage indicates a rough estimate of the prevalence of plantation within apolygon (as in the case of a mosaic). Types are defined as follows:

Large industrial plantation: single plantation units larger than 100 hectares

Mosaic of medium-sized plantations: mosaic of plantation units < 100 hectares embedded within patches of other land use

Mosaic of small-sized plantations: mosaic of plantation units < 10 hectares embedded within patches of other land use.

Clearing/ very young plantation: bare ground with contextual clues suggesting it will become a plantations (shape or pattern of clearing, proximity to other plantations, distinctive road network, etc)

For more information on this data set and how it was produced, see the forthcoming WRI Technical Note associated with this project.

Overview

This data set was created by Transparent World, with the support of Global Forest Watch. Many studies depicting forest cover and forest change cannot distinguish between natural forests and plantations. This data set attempts to distinguish tree plantations from natural forest for seven key countries: Brazil, Cambodia, Colombia, Indonesia, Liberia, Malaysia, and Peru.

Given the variability of plantations and their spectral similarity to natural forests, this study used visual interpretations of satellite imagery, primarily Landsat, supplemented by high resolution imagery (Google Maps, Bing Maps, or Digital Globe), where available, to locate plantations. Analysts hand-digitized plantation boundaries based on several key visual criteria, including texture, shape, color, and size.

Each polygon is labelled with the plantation type and when possible, the species. A “gr” in front of the species name indicates a group of species, such as pines or fruit, where the individual species was not identifiable. The percentage of plantation coverage indicates a rough estimate of the prevalence of plantation within apolygon (as in the case of a mosaic). Types are defined as follows:

Large industrial plantation: single plantation units larger than 100 hectares

Mosaic of medium-sized plantations: mosaic of plantation units < 100 hectares embedded within patches of other land use

Mosaic of small-sized plantations: mosaic of plantation units < 10 hectares embedded within patches of other land use.

Clearing/ very young plantation: bare ground with contextual clues suggesting it will become a plantations (shape or pattern of clearing, proximity to other plantations, distinctive road network, etc)

For more information on this data set and how it was produced, see the forthcoming WRI Technical Note associated with this project.

Overview

This data set was created by Transparent World, with the support of Global Forest Watch. Many studies depicting forest cover and forest change cannot distinguish between natural forests and plantations. This data set attempts to distinguish tree plantations from natural forest for seven key countries: Brazil, Cambodia, Colombia, Indonesia, Liberia, Malaysia, and Peru.

Given the variability of plantations and their spectral similarity to natural forests, this study used visual interpretations of satellite imagery, primarily Landsat, supplemented by high resolution imagery (Google Maps, Bing Maps, or Digital Globe), where available, to locate plantations. Analysts hand-digitized plantation boundaries based on several key visual criteria, including texture, shape, color, and size.

Each polygon is labelled with the plantation type and when possible, the species. A “gr” in front of the species name indicates a group of species, such as pines or fruit, where the individual species was not identifiable. The percentage of plantation coverage indicates a rough estimate of the prevalence of plantation within apolygon (as in the case of a mosaic). Types are defined as follows:

Large industrial plantation: single plantation units larger than 100 hectares

Mosaic of medium-sized plantations: mosaic of plantation units < 100 hectares embedded within patches of other land use

Mosaic of small-sized plantations: mosaic of plantation units < 10 hectares embedded within patches of other land use.

Clearing/ very young plantation: bare ground with contextual clues suggesting it will become a plantations (shape or pattern of clearing, proximity to other plantations, distinctive road network, etc)

For more information on this data set and how it was produced, see the forthcoming WRI Technical Note associated with this project.

Overview

This data set was created by Transparent World, with the support of Global Forest Watch. Many studies depicting forest cover and forest change cannot distinguish between natural forests and plantations. This data set attempts to distinguish tree plantations from natural forest for seven key countries: Brazil, Cambodia, Colombia, Indonesia, Liberia, Malaysia, and Peru.

Given the variability of plantations and their spectral similarity to natural forests, this study used visual interpretations of satellite imagery, primarily Landsat, supplemented by high resolution imagery (Google Maps, Bing Maps, or Digital Globe), where available, to locate plantations. Analysts hand-digitized plantation boundaries based on several key visual criteria, including texture, shape, color, and size.

Each polygon is labelled with the plantation type and when possible, the species. A “gr” in front of the species name indicates a group of species, such as pines or fruit, where the individual species was not identifiable. The percentage of plantation coverage indicates a rough estimate of the prevalence of plantation within apolygon (as in the case of a mosaic). Types are defined as follows:

Large industrial plantation: single plantation units larger than 100 hectares

Mosaic of medium-sized plantations: mosaic of plantation units < 100 hectares embedded within patches of other land use

Mosaic of small-sized plantations: mosaic of plantation units < 10 hectares embedded within patches of other land use.

Clearing/ very young plantation: bare ground with contextual clues suggesting it will become a plantations (shape or pattern of clearing, proximity to other plantations, distinctive road network, etc)

For more information on this data set and how it was produced, see the forthcoming WRI Technical Note associated with this project.

Overview

This data set was created by Transparent World, with the support of Global Forest Watch. Many studies depicting forest cover and forest change cannot distinguish between natural forests and plantations. This data set attempts to distinguish tree plantations from natural forest for seven key countries: Brazil, Cambodia, Colombia, Indonesia, Liberia, Malaysia, and Peru.

Given the variability of plantations and their spectral similarity to natural forests, this study used visual interpretations of satellite imagery, primarily Landsat, supplemented by high resolution imagery (Google Maps, Bing Maps, or Digital Globe), where available, to locate plantations. Analysts hand-digitized plantation boundaries based on several key visual criteria, including texture, shape, color, and size.

Each polygon is labelled with the plantation type and when possible, the species. A “gr” in front of the species name indicates a group of species, such as pines or fruit, where the individual species was not identifiable. The percentage of plantation coverage indicates a rough estimate of the prevalence of plantation within apolygon (as in the case of a mosaic). Types are defined as follows:

Large industrial plantation: single plantation units larger than 100 hectares

Mosaic of medium-sized plantations: mosaic of plantation units < 100 hectares embedded within patches of other land use

Mosaic of small-sized plantations: mosaic of plantation units < 10 hectares embedded within patches of other land use.

Clearing/ very young plantation: bare ground with contextual clues suggesting it will become a plantations (shape or pattern of clearing, proximity to other plantations, distinctive road network, etc)

For more information on this data set and how it was produced, see the forthcoming WRI Technical Note associated with this project.

Overview

This data set was created by Transparent World, with the support of Global Forest Watch. Many studies depicting forest cover and forest change cannot distinguish between natural forests and plantations. This data set attempts to distinguish tree plantations from natural forest for seven key countries: Brazil, Cambodia, Colombia, Indonesia, Liberia, Malaysia, and Peru.

Given the variability of plantations and their spectral similarity to natural forests, this study used visual interpretations of satellite imagery, primarily Landsat, supplemented by high resolution imagery (Google Maps, Bing Maps, or Digital Globe), where available, to locate plantations. Analysts hand-digitized plantation boundaries based on several key visual criteria, including texture, shape, color, and size.

Each polygon is labelled with the plantation type and when possible, the species. A “gr” in front of the species name indicates a group of species, such as pines or fruit, where the individual species was not identifiable. The percentage of plantation coverage indicates a rough estimate of the prevalence of plantation within apolygon (as in the case of a mosaic). Types are defined as follows:

Large industrial plantation: single plantation units larger than 100 hectares

Mosaic of medium-sized plantations: mosaic of plantation units < 100 hectares embedded within patches of other land use

Mosaic of small-sized plantations: mosaic of plantation units < 10 hectares embedded within patches of other land use.

Clearing/ very young plantation: bare ground with contextual clues suggesting it will become a plantations (shape or pattern of clearing, proximity to other plantations, distinctive road network, etc)

For more information on this data set and how it was produced, see the forthcoming WRI Technical Note associated with this project.

The accuracy of this layer was measured at 88% overall, with Kappa values of 0.81 and 0.83 for the categories of Forest and Non-Forest, respectively.Se estima que la precisión de esta capa es de 88% del total, con los valores Kappa de 0.81 y 0.83 para las categorías de Bosque y No Bosque, respectivamente

Overview

This data set comes from the National Institute of Forests ( INAB) in Guatemala. Through various joint efforts and in coordination with the Inter-institutional Group for Forest Monitoring and the GIZ, INAB obtained 308 high resolution RapidEye (RE) images to cover the entire country. These images, with a spatial resolution of 5 meters multispectral, were used to detail 16 classes of forest, 21 subtypes of forest, and 16 subtypes of forest by density. For broadleaf, coniferous, and mixed forest, detailed densities (sparse and dense) were differentiated for the first time in Guatemala.

Mangroves were identified at the species level thanks to the database of Project Mangrove, 2012 MARN-CATHALAC, which has registers of four species. For the purposes of this map, un-forested zones were simply designated “No Forest”.

The accuracy of this layer was measured at 88% overall, with Kappa values of 0.81 and 0.83 for the categories of Forest and Non-Forest, respectively.Se estima que la precisión de esta capa es de 88% del total, con los valores Kappa de 0.81 y 0.83 para las categorías de Bosque y No Bosque, respectivamente

Overview

This data set comes from the National Institute of Forests ( INAB) in Guatemala. Through various joint efforts and in coordination with the Inter-institutional Group for Forest Monitoring and the GIZ, INAB obtained 308 high resolution RapidEye (RE) images to cover the entire country. These images, with a spatial resolution of 5 meters multispectral, were used to detail 16 classes of forest, 21 subtypes of forest, and 16 subtypes of forest by density. For broadleaf, coniferous, and mixed forest, detailed densities (sparse and dense) were differentiated for the first time in Guatemala.

Mangroves were identified at the species level thanks to the database of Project Mangrove, 2012 MARN-CATHALAC, which has registers of four species. For the purposes of this map, un-forested zones were simply designated “No Forest”.

There was no accuracy assessment done for this layer.No existe un estudio de precisión para esta capa.

Overview

This data set comes from the National Institute of Forests (INAB) in Guatemala. The general objective of this project was to update the national forest cover map to the year 2006. The specific objectives included calculating the rates of land cover changes at national, departmental, and municipal scales and comparing the 2006 map to the 2001 map that was generated with the same methodology. The new methodology used to generate the 2006 map made it necessary to create a new 2001 map. To describe forest cover at the national scale, the use of information from remote sensing, whether satellite images or aerial photographs, represented the most accurate data source.

The elaboration of national maps of forest cover was based on satellite images from Landsat 5 and 7 as well as ASTER (only for the southwestern section of the country, in the area corresponding to the Landsat image of Path 21 Row 50). The forest cover for Guatemala in 2006 was estimated as 3,866,383 hectares, equivalent to 35.5% of the national territory. The revised value for 2001 is 4,152,051 hectares corresponding to 38.1% of the national territory. These values represent an annual net loss of 48,084 hectares, equivalent to a deforestation rate of - 1.16%. The net annual loss reported is the difference between a gross loss of 101,852 hectares/year and a gain of 53,768 hectares/year.

The national forest cover map for 2010 and the forest dynamics from 2006-2010 have a 91% level of accuracy according to an evaluation done by an external team.El mapa nacional de cobertura forestal 2010 y de dinámica forestal 2006-2010 cuenta con un grado de precisión del 91%, de acuerdo con una evaluación de la misma a cargo de un equipo externo.

Overview

This data set comes from the National Institute of Forests (INAB) in Guatemala. Information from Landsat 5 and Landsat 7 was used as the primary source of information, corresponding to 2010, although in certain areas images from 2009 and 2011 were used based on their quality. As secondary sources of information, information from 2006 orthophotos and images for 2010 from the ALOS-PRISM sensor were used.

Forest cover in 2010 for Guatemala was estimated at 3,722,595 hectares, equivalent to 34% of the national land territory. The value for forest cover for 2006, published in May of 2011, was revised and a new estimate of 3,868,708 hectares was obtained. These values represent a net loss of 146,112 hectares of forest, equivalent to a net deforestation rate of -1.0% per year at the national level (with respect to the total existing forest in 2006). The results show that the net rate of deforestation continues falling compared to previous studies (-1.43% for 1991-2001 and -1.16% for 2001-2006) as a result of substantial increases in forest cover gain. However, gross deforestation continues to increase, reaching an area of 132,137 hectares per year for the analyzed time period.

52% of the national forest cover is located inside the Guatemalan System of Protected Areas (SIGAP), which covers a third of the national territory. The remaining 48% of the forest cover is distributed in the other two thirds of the country. In face of this concentration of forest cover inside of Protected Areas, three quarters of the loss during 2006-2010 occurred inside of SIGAP and one quarter outside of SIGAP.

Displays boundaries of forested areas allocated by governments to companies for harvesting timber and other wood products.

RESOLUTION / SCALE

Varies by country

Geographic coverage

Currently available for Cameroon, Canada, Central African Republic, Democratic Republic of the Congo (DRC), Equatorial Guinea, Gabon, Indonesia, Liberia, and Republic of the Congo.

Source data

Generally based on a combination of government documents, satellite imagery, and GPS data. For information on country-specific concessions data please refer to the Data page.

Frequency of updates

Variable, depending on government agencies in each country and other data providers

Date of content

Varies by country

Cautions

This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not be comprehensive of all existing concessions in a country, and the location of certain concessions can be inaccurate.

Overview

“Managed forests” refers to areas allocated by a government for harvesting timber and other wood products in a public forest. Managed forests are distinct from wood fiber concessions, where tree plantations are established for the exclusive production of pulp and paper products. “Concession” is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract timber and other wood products from public forests; terminology varies at the national level, however, and includes "forest permits," "tenures," "licenses," and other terms.

This data set displays managed forest concessions as a single layer assembled by aggregating data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. For more information on concession data for each country please visit the Open Data Portal.

If you are aware of concession data for additional countries, please email us here.

Logging in Cameroon’s forests is permitted within Forest Management Units (FMUs). This data set displays the FMUs within Cameroon’s permanent forest estate. Selective logging is permitted in Cameroon’s FMUs, which are further divided into logging concessions called annual allowable cuts (AACs). These logging permits require owners and operators to maintain permanent forest cover. This data set was produced in collaboration between the Cameroon Ministry of Forestry and Wildlife and WRI. For more information and data sets, see the Interactive Forest Atlas for Cameroon.

Credit: Cameroon Ministry of Forestry and Wildlife, World Resources Institute.

This data set provides the boundaries of forest areas licensed to companies for forestry and is a compilation of provincial forest tenure data sets across Canada. Tenures for Nova Scotia are not provided, due to changes underway in forest management in that province, and will be added to this data set when available. Much of Canada’s logging activity occurs on Crown (often referred to as “public”) land and is regulated by various provincial commercial forest tenure systems that allocate cutting rights to and confer obligations on recipients of the tenures. It is these tenure systems on Crown forest land that are the focus of this data product. The information in this data set was gathered in order to continue to develop an understanding of how much of Canada’s forest area is under commercial forest tenures, where these tenures (including the operating areas of major forest companies) are located, and who is most likely to control them. This information and these data sets are important because of the extent of tenures and the resulting logging activity over vast areas of Canada’s Crown forest land.

This data set provides the permit boundaries for selective logging in the Central African Republic’s production forests. This data set was produced through a collaboration between the CAR Ministry of Water, Forests, Hunting, and Fishing (MEFCP) and WRI. For more information, see the Interactive Forest Atlas for the Central African Republic.

Credit: Central African Republic Ministry of Water and Forests, Hunting, and Fishing; German Technical Cooperation (GIZ); French Development Agency (AFD); Special Allocation Fund for Forest Development (CASDF); World Resources Institute

Forest concession data for the Democratic Republic of the Congo (DRC) represent geographic areas permitted for exploitation of timber by selective logging. This data set was produced through a collaboration between the DRC Ministry of Environment, Nature Conservation, and Tourism (MECNT) and WRI. For more information, see the Interactive Forest Atlas for the Democratic Republic of the Congo

This data set provides the boundaries for logging concessions for Equatorial Guinea, where commercial forestry is permitted. The Equatorial Guinea Ministry of Agriculture and Forests and the World Resources Institute produced this information in a collaboration for the Interactive Forest Atlas of Equatorial Guinea. Information provided in the data set includes the boundaries of concessions, the operating company, the ownership group, documentation on the permit process, and other information.

Credit: Equatorial Guinea Ministry of Agriculture and Forests and the World Resources Institute, 2013

This data set provides the boundaries for forest licenses in Gabon. The licenses cover areas permitted for selective logging. This data set was produced in a collaboration between the Gabon Ministry of Forest Economy, Water, Fisheries, and Aquaculture (MEFEPA) and WRI. For more information, see the Interactive Forest Atlas for Gabon.

This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of logging concessions for the selective logging of natural forests in Indonesia. According to this data set, there are 557 active logging concessions in Indonesia.

The Liberia logging concessions data set combines the boundaries of forest management contracts, timber sale contracts, and private use permits, compiled by Global Witness from available government and contractual maps. Due to a lack of quality data, this data set should be used for demonstration purposes only and not for land use planning purposes. Forests in Liberia are managed by the state under the National Forestry Reform Law, enacted in October 2006. Following the enactment of this law, UN Security Council timber export sanctions against Liberia were lifted, permitting commercial forestry activities to resume. Forest management contracts include concession areas of 50,000 hectares to 400,000 hectares and are open for bids from qualified bidders that demonstrate at least 51% ownership by Liberian citizens; concessions of more than 100,000 hectares are open for bidding from international investors. Timber sale contracts are established through a bidding process for areas up to 5,000 hectares; bidders must demonstrate at least 51% ownership by Liberian citizens. Private Use Permits are licenses issued to private landowners to extract wood and lack sustainability regulations. Global Witness compiled the logging concession data as part of the report Signing Their Lives Away: Liberia’s Private Use Permits and the Destruction of Community Owned Rainforest (Global Witness, 2012).

Forest concession data for the Republic of the Congo provide the boundaries of areas permitted for selective logging. These areas, also called Forest Exploitation Units, are managed in accordance with the national Forest Code. This data set was produced through a collaboration between the Republic of the Congo Ministry of Forest Economy and WRI. For more information, see the Interactive Forest Atlas for the Republic of the Congo.

Credit: Republic of the Congo Ministry of Forest Economy (MEF), National Center for Inventory and Planning of Forest and Wildlife Resources (CNIAF), World Resources Institute

Canada forest tenures

Overview

This data set provides the boundaries of forest areas licensed to companies for forestry and is a compilation of provincial forest tenure data sets across Canada.

Tenures for Nova Scotia are not provided, due to changes underway in forest management in that province, and will be added to this data set when available. Much of Canada’s logging activity occurs on Crown (often referred to as “public”) land and is regulated by various provincial commercial forest tenure systems that allocate cutting rights to and confer obligations on recipients of the tenures. It is these tenure systems on Crown forest land that are the focus of this data product. The information in this data set was gathered in order to continue to develop an understanding of how much of Canada’s forest area is under commercial forest tenures, where these tenures (including the operating areas of major forest companies) are located, and who is most likely to control them. This information and these data sets are important because of the extent of tenures and the resulting logging activity over vast areas of Canada’s Crown forest land.

Overview

Logging in Cameroon’s forests is permitted within Forest Management Units (FMUs). This data set displays the FMUs within Cameroon’s permanent forest estate. Selective logging is permitted in Cameroon’s FMUs, which are further divided into logging concessions called annual allowable cuts (AACs). These logging permits require owners and operators to maintain permanent forest cover. This data set was produced in collaboration between the Cameroon Ministry of Forestry and Wildlife and WRI. For more information and data sets, see the Interactive Forest Atlas for Cameroon.

Credit:Cameroon Ministry of Forestry and Wildlife, World Resources Institute.

Overview

This data set provides the permit boundaries for selective logging in the Central African Republic’s production forests. This data set was produced through a collaboration between the CAR Ministry of Water, Forests, Hunting, and Fishing (MEFCP) and WRI. For more information, see the Interactive Forest Atlas for the Central African Republic.

Credit:Central African Republic Ministry of Water and Forests, Hunting, and Fishing; German Technical Cooperation (GIZ); French Development Agency (AFD); Special Allocation Fund for Forest Development (CASDF); World Resources Institute

Overview

Forest concession data for the Democratic Republic of the Congo (DRC) represent geographic areas permitted for exploitation of timber by selective logging. This data set was produced through a collaboration between the DRC Ministry of Environment, Nature Conservation, and Tourism (MECNT) and WRI. For more information, see the Interactive Forest Atlas for the Democratic Republic of the Congo.

Overview

This data set provides the boundaries for logging concessions for Equatorial Guinea, where commercial forestry is permitted. The Equatorial Guinea Ministry of Agriculture and Forests and the World Resources Institute produced this information in a collaboration for the Interactive Forest Atlas of Equatorial Guinea. Information provided in the data set includes the boundaries of concessions, the operating company, the ownership group, documentation on the permit process, and other information.

Credit:Equatorial Guinea Ministry of Agriculture and Forests and the World Resources Institute, 2013

Overview

This data set provides the boundaries for forest licenses in Gabon. The licenses cover areas permitted for selective logging. This data set was produced in a collaboration between the Gabon Ministry of Forest Economy, Water, Fisheries, and Aquaculture (MEFEPA) and WRI. For more information, see the Interactive Forest Atlas for Gabon.

Indicates the area of Indonesia’s moratorium against new forest concessions, designed to protect Indonesia’s peat lands and primary natural forests from future development

Geographic coverage

Indonesia

Source data

May and November 2014 versions of the Indicative Moratorium Map (IMM) by the Indonesia Ministry of Forestry

Frequency of updates

Every 6 months

Date of content

2014

Cautions

This data is a combination of version 7 (November 2014) data for 11 priority provinces, and version 6 (May 2014) data for the remaining provinces, which had no available update. Version 7 is the newest version available to the public. Updates to the moratorium area in 2015 have not yet been published.

Overview

In May 2011, the Ministry of Forestry put into effect a two-year moratorium on the designation of new forest concessions in primary natural forests and peatlands. This moratorium is designed to allow time for the government to develop improved processes for land-use planning, strengthen information systems, and build institutions to achieve Indonesia’s low emission development goals. The two-year moratorium, renewed again in May 2015, is part of Indonesia’s pledge to curtail forest clearing in a US $1 billion deal with the Norwegian government.

The first Indicative Moratorium Map (IMM) was published by the Ministry of Forestry in July 2011. The IMM is required to be revised every six months. This data set shows the version 7 IMM (November 2014) for 11 priority provinces (Aceh, Jambi, Central Kalimantan, East Kalimantan, West Kalimantan, Central Sulawesi, South Sumatra, West Sumatra, West Papua, Papuan, and Riau), and the version 6 IMM (May 2014) for remaining provinces. The Ministry of Forestry has revised the map since version 7, but these data are not available to the public.

Overview

This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of logging concessions for the selective logging of natural forests in Indonesia. According to this data set, there are 557 active logging concessions in Indonesia.

Overview

The Liberia logging concessions data set combines the boundaries of forest management contracts (FMCs) and timber sale contracts (TSCs), compiled by Global Witness from available government and contractual maps.

Due to a lack of quality data, this data set should be used for demonstration purposes only and not for land use planning purposes. Forests in Liberia are managed by the state under the National Forestry Reform Law, enacted in October 2006. Following the enactment of this law, UN Security Council timber export sanctions against Liberia were lifted, permitting commercial forestry activities to resume. Forest management contracts include concession areas of 50,000 hectares to 400,000 hectares and are open for bids from qualified bidders that demonstrate at least 51% ownership by Liberian citizens; concessions of more than 100,000 hectares are open for bidding from international investors. Timber sale contracts are established through a bidding process for areas up to 5,000 hectares; bidders must demonstrate at least 51% ownership by Liberian citizens.

Overview

Forest concession data for the Republic of the Congo provide the boundaries of areas permitted for selective logging. These areas, also called Forest Exploitation Units, are managed in accordance with the national Forest Code. This data set was produced through a collaboration between the Republic of the Congo Ministry of Forest Economy and WRI. For more information, see the Interactive Forest Atlas for the Republic of the Congo.

Credit:Republic of the Congo Ministry of Forest Economy (MEF), National Center for Inventory and Planning of Forest and Wildlife Resources (CNIAF), World Resources Institute.

Overview

This map was created from two State Forestry Department (SFD) 2010 maps, supplemented by information from Environmental Impact Assessments (EIAs) for re-entry logging by specific licensees. Where not already identified in available SFD maps, names of licensees associated with individual numbered licenses have (where possible) been obtained from company documents and regional perspective maps from EIAs.

Permit issuance dates and official areas in hectares are from various sources, including EIAs. Identities of corporate groupings to which individual licensee companies belong are based on various sources, including EIAs and official company documents.

This map does not include Timber Licenses in Sarikei, Betong, Sri Aman, Samarahan and Kuching Divisions in the west of Sarawak, though there are known to be few such licenses in those Divisions. It is possible that some licenses shown on this map may have expired or been amended since 2010; other new licenses not shown here may also have been created. Individual licensees may also have joined or split from the named corporate groups. This map also does not include 'Belian Timber' Licenses.

A number of the Timber Licenses are split into non-contiguous parts. In some cases separate boundary entries are given for each part; these are annotated 'Part 1' etc in the title. These 'Part' are not official names, but rather naming conventions used in the GIS cleaning of the data.

Displays boundaries of areas allocated by governments to companies for extraction of minerals

RESOLUTION / SCALE

Varies by country

Geographic coverage

Currently available for Cameroon, Cambodia, Canada, Colombia, Republic of the Congo, Democratic Republic of the Congo (DRC), and Gabon

Source data

Generally based on a combination of government documents, satellite imagery, and GPS data. For information on country-specific concessions data please refer to the Data page.

Frequency of updates

Variable, depending on government agencies in each country and other data providers

Date of content

Varies by country

Cautions

This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.

Overview

“Mining concession” refers to an area allocated by a government or other body for the extraction of minerals. The terminology for these areas varies from country to country. “Concession” is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract minerals from public lands; terminology varies at the national level, however, and includes mineral or mining "permits," "tenures," "licenses," and other terms.

This data set displays mining concessions as a single layer assembled by aggregating concession data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. For more information on concession data for each country please visit the Open Data Portal.

If you are aware of concession data for additional countries, please email us here.

Suggested citation for data as displayed on GFW: “Mining.” World Resources Institute. Accessed through Global Forest Watch on [date]. www.globalforestwatch.org.

This data set provides the boundaries of prospective mining permits for Cameroon, as well as the type of mineral resource. Map data were provided by the Cameroon Ministry of Mines and Technological Development and published in a collaboration with the World Resources Institute. For more information and data sets, see the Interactive Forest Atlas for Cameroon.

This data set provides the boundaries of mining titles (títulos mineros concedidos) for Colombia. The shapefiles are compiled by Tierra Minada, a Colombian civil society group, utilizing information from the Colombian Mining Registry, which is maintained by the National Mining Agency. For more information about the data sets, visit the Tierra Minada website or Colombia’s Mining Cadaster Portal.

Credit: Tierra Minada; Agencia Nacional de Minería de Colombia.

This data set provides the boundaries for mining permits in the Democratic Republic of the Congo. This data set is available from the Ministry of Mines Mining Registry (CAMI), for purchase and could not be made available for public download. For more information, see the DRC's mining cadaster portal.

This data set provides the boundaries for mining permits and prospecting permits for Gabon. The data set was produced by the Gabon Ministry of Mines, Petroleum, and Hydrocarbons and the World Resources Institute for the Interactive Mining, Forest, and Conservation Atlas of Gabon. For more information, see the Interactive Forest Atlas of Gabon.

This data set provides the boundaries of mining permits for the Republic of the Congo. The original map data were produced by the Republic of the Congo Ministry of Mines and Geology with the support of the World Resources Institute. For more information, see the Interactive Forest Atlas for the Republic of the Congo.

Credit: Republic of the Congo Ministry of Mines and Geology, World Resources Institute.

Data is aggregated by RAISG - the Amazon Socio-environmental Georeferenced Information Network. Partner organizations from each country contribute the data.

Bolivia: SERGEOTECMIN, 2005

Brazil: Departamento Nacional da Produção Mineral-DNPM, 2011

Colombia: Catastro Minero Colombiano, 2010

Ecuador: Ministerio de Recursos Naturales no Renovables, 2010

Guyana: Guyana Geology and Mines Commission, 2009

Peru: MINEM, 2011

Suriname: Natural Resource and Environmental Assessment - NARENA

Venezuela: Ministerio de Energía y Minas, 2009

Frequency of updates

Varies by country

Date of content

Varies by country

Cautions

Some countries provide data for the entire country (Bolivia, Guyana, Suriname, Venezuela), while others only provide land rights data for the Amazon portion of their country (Brazil, Colombia, Ecuador, Peru).

Unfortunately, RAISG data is not downloadable or analyzable on Global Forest Watch at this time.

Overview

RAISG is a network of organizations with the goal of sharing georeferenced socioeconomic data throughout the Amazon Basin. This data set shows the boundaries of mining concessions in Bolivia, the Brazilian Amazon, the Colombian Amazon, the Ecuadorian Amazon, Guyana, the Peruvian Amazon, Suriname, and Venezuela. The territories are classified according to their status: potential, in application, exploration, exploitation/ exploration, exploitation, and unknown. Unfortunately, RAISG data is not downloadable or analyzable on Global Forest Watch at this time. To see the data in the original web map, click here.

Overview

The inalienable Aboriginal freehold lands data set represents boundary and attribute information for land parcels, granted to incorporated Aboriginal groups through the Aboriginal Land Rights (Northern Territory) Act of 1976, which are greater than 40 hectares. Aboriginal land is private property owned under special freehold title. It is inalienable, meaning it cannot be bought, acquired, or forfeited. The inalienable Aboriginal freehold is granted as a communal title (where land is held collectively by a group, rather than by individuals), and is the strongest form of title in Australia. This title gives Aboriginal groups the power to control the direction and pace of development on their land. The data set only includes Aboriginal lands that are officially registered. More information is available at: http://www.ga.gov.au/metadata-gateway/metadata/record/42339/.

Overview

This data set provides the boundaries for the following industrial concessions across Canada: active mineral prospecting permits across the Northwest Territories and Nunavut as of July 2012; Active mineral leases as of February 2013; Active mineral claims as of February 2013.Active coal tenures as of February 2013. Data was obtained from provincial and federal government websites and may be subject to use limitations listed with those sources. This data compilation was completed by Global Forest Watch Canada.

Overview

This layer shows mining concessions for Brazil from the National Department of Mineral Production (DNPM), downloaded November 2015 from the SIGMINE web portal. The polygons in this layer represent approved concessions for mineral exploration and extraction, as well as concessions in application or available concessions.

Overview

This data set provides the boundaries of prospective mining permits for Cameroon, as well as the type of mineral resource. Map data were provided by the Cameroon Ministry of Mines and Technological Development and published in a collaboration with the World Resources Institute. For more information and data sets, see the Interactive Forest Atlas for Cameroon.

Overview

This data set provides the boundaries of mining titles (títulos mineros concedidos) for Colombia. The shapefiles are compiled by Tierra Minada, a Colombian civil society group, utilizing information from the Colombian Mining Registry, which is maintained by the National Mining Agency. For more information about the data sets, visit the Tierra Minada website or Colombia’s Mining Cadaster Portal.

Overview

This data set provides the boundaries for mining permits in the Democratic Republic of the Congo. This data set is available from the Ministry of Mines Mining Registry (CAMI), for purchase and could not be made available for public download. For more information, see the DRC's mining cadaster portal.

Overview

This data set provides the boundaries for mining permits and prospecting permits for Gabon. The data set was produced by the Gabon Ministry of Mines, Petroleum, and Hydrocarbons and the World Resources Institute for the Interactive Mining, Forest, and Conservation Atlas of Gabon. For more information, see the Interactive Forest Atlas of Gabon.

Overview

This data set provides the boundaries of mining permits for the Republic of the Congo. The original map data were produced by the Republic of the Congo Ministry of Mines and Geology with the support of the World Resources Institute. For more information, see the Interactive Forest Atlas for the Republic of the Congo.

Credit:Republic of the Congo Ministry of Mines and Geology, World Resources Institute.

This data set contains all of the data that is currently available to Open Development Cambodia (ODC) and is not exhaustive. Projects or areas with publicly known boundaries are mapped as polygons; those without publicly known boundaries are represented as dots on the map. The dots are placed in the center of the closest known geographical area and thus do not represent exact locations. The development landscape is constantly changing, and there are also additional developments for which data is not available. While ODC takes every effort to ensure that the details in this map are accurate and up to date, some of the projects or areas marked on the map may have since been modified or cancelled since the map was published. Moreover, additional developments may have been approved that are not yet included here.

Overview

The mining sector in Cambodia is mostly undeveloped, and active mining enterprises are typically small-scale quarries producing materials for construction, such as laterite, marble, granite, limestone, gravel and sand. There are also thousands of artisanal miners recovering gold and gemstones, often on a seasonal or part-time basis. There is no industrial-scale extraction of minerals, although many exploration licenses have been granted to mining companies and some have reported promising finds of gold. The mining law states that all mining in “national cultural, historical and heritage sites” is prohibited, and mining activities in “protected, reserved or restricted” areas can only be carried out with written permission of the authority responsible. The 2002 Law on Forestry allows mining within the permanent forest estate, however, any proposed mining operation must be the subject of a ‘prior study-evaluation’ by the Ministry of Agriculture, Forestry and Fisheries (MAFF). The legality of mining operations on lands traditionally managed by indigenous people requires clarification.

This dataset contains data for mining licenses in Cambodia with contract dates starting from 1995 to 2014. Due to the lack of publicly available information, this dataset does not include information on implementation status of mines or change in ownership of mining licenses.

Open Development Cambodia collected the data from a variety of public domain sources such as the government, non-governmental organizations (NGOs), research institutes, company websites, and news reports.

Displays boundaries of areas allocated by governments to companies for tree plantations

RESOLUTION / SCALE

Varies by country

Geographic coverage

Currently available for Cameroon, Gabon, Indonesia, Liberia, and Republic of the Congo

Source data

Generally based on a combination of government documents, satellite imagery, and GPS data. For information on country-specific concessions data please refer to the Data page.

Frequency of updates

Variable, depending on government agencies in each country and other data providers

Date of content

Varies by country

Cautions

This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.

Overview

“Plantation” refers to an area allocated by a government or other body for the establishment of fast growing tree plantations for the production of oil palm, timber, or other wood products, including pulp and paper.

This data set displays plantation concessions as a single layer assembled by aggregating data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. For more information on concession data for each country please visit the Open Data Portal.

If you are aware of plantation concession data for additional countries, please email us here.

Suggested citation for data as displayed on GFW: “Plantations.” World Resources Institute. Accessed through Global Forest Watch on [date]. www.globalforestwatch.org.

This data layer shows the boundaries of agro-industrial zones, where oil palm and rubber tree plantations, as well as other crops, may be established. In Cameroon, industrial agriculture falls outside of the National Forest Estate. Agricultural plantations are allocated by the Ministry of Economy and Planning to private entities under long-term, renewable contracts, which are then monitored by the Ministry of Agriculture. The agro-industrial data set was mapped using satellite imagery, with ground-truthing to determine the crop type and operating company. Official documentation was often lacking, so boundaries should be considered approximate and nonexhaustive.

This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of current or planned oil palm plantations in Indonesia. This data set is known to be incomplete, but it is currently the best available.

This data set provides the boundaries of known oil palm plantations for Liberia and was compiled by Global Witness from available government maps. Information provided with this data set includes company, ownership group, and land area.

Displays boundaries of areas allocated by governments to companies for oil palm plantations

RESOLUTION / SCALE

Varies by country

Geographic coverage

Currently available for Cameroon, Republic of the Congo, Indonesia, and Liberia

Source data

Generally based on a combination of government documents, satellite imagery, and GPS data. For information on country-specific concessions data please refer to the Data page.

Frequency of updates

Variable, depending on government agencies in each country and other data providers

Date of content

Varies by country

Cautions

This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.

Overview

“Oil palm concession” refers to an area allocated by a government or other body for industrial-scale oil palm plantations.

This data set displays oil palm concessions as a single layer assembled by aggregating concession data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. For more information on concession data for each country please visit the Open Data Portal.

If you are aware of concession data for additional countries, please email us here.

This data layer shows the boundaries of agro-industrial zones, where oil palm and rubber tree plantations, as well as other crops, may be established. In Cameroon, industrial agriculture falls outside of the National Forest Estate. Agricultural plantations are allocated by the Ministry of Economy and Planning to private entities under long-term, renewable contracts, which are then monitored by the Ministry of Agriculture. The agro-industrial data set was mapped using satellite imagery, with ground-truthing to determine the crop type and operating company. Official documentation was often lacking, so boundaries should be considered approximate and nonexhaustive.

This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of current or planned oil palm plantations in Indonesia. This data set is known to be incomplete, but it is currently the best available.

This data set provides the boundaries of known oil palm plantations for Liberia and was compiled by Global Witness from available government maps. Information provided with this data set includes company, ownership group, and land area.

Overview

This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of current or planned oil palm plantations in Indonesia. This data set is known to be incomplete, but it is currently the best available.

Overview

This data layer shows the boundaries of agro-industrial zones, where oil palm and rubber tree plantations, as well as other crops, may be established. In Cameroon, industrial agriculture falls outside of the National Forest Estate. Agricultural plantations are allocated by the Ministry of Economy and Planning to private entities under long-term, renewable contracts, which are then monitored by the Ministry of Agriculture. The agro-industrial data set was mapped using satellite imagery, with ground-truthing to determine the crop type and operating company. Official documentation was often lacking, so boundaries should be considered approximate and nonexhaustive.

Overview

This data set provides the boundaries of known oil palm plantations for Liberia and was compiled by Global Witness from available government maps. Information provided with this data set includes company, ownership group, and land area.

Shows the coverage of oil palm concessions in Sarawak, where information is available

Geographic coverage

Sarawak, Malaysia

Source data

SADIA, Aidenvironment & Earthsight Investigations

Date of content

2010

Cautions

This map is known to be incomplete and some of the boundaries imprecise. Oil palm concessions which were issued or published EIAs after 2010 are not included. Boundaries drawn from EIA reports have not been mapped precisely, and so may not match with clearance and planting visible in satellite imagery. Boundaries and associated information relating to those concessions which are included may also have changed since the source information was published.

Overview

This data set provides the boundaries of known oil palm concessions for the state of Sarawak, Malaysia, and was compiled from available public documents. Where available, associated information provided with this data set includes licensee name, permit number (Operational Ticket), corporate grouping, permit date, and official license area. This data set also includes areas under License for Planted Forest (LPF) agreements where oil palm has been planted for one rotation, where available

The data set combines the boundaries of oil palm licenses obtained from Environmental Impact Assessment (EIA) reports for individual oil palm and LPF concessions (spanning various dates) and a regional State Forestry Department map from 2010 for northern Sarawak. The information drawn from oil palm EIA reports was compiled by Aidenvironment and SADIA, and combined with the other information by Earthsight Investigations. Licenses (and licensee subsidiaries) have been attributed to major corporate groups based on published company records and secondary sources.

A number of the oil palm concession licenses are split into non-contiguous parts. In some cases separate boundary entries are given for each part; these are annotated 'Part 1' etc in the title. These 'Parts' are not official names, but rather naming conventions used in the GIS cleaning of the data.

Displays boundaries of areas allocated by governments to private companies for tree plantations for production of timber and wood pulp for paper and paper products

RESOLUTION / SCALE

Varies by country

Geographic coverage

Currently available for Republic of the Congo, Gabon, and Indonesia

Source data

Generally based on a combination of government documents, satellite imagery, and GPS data. For information on country-specific concessions data please refer to the Data page.

Frequency of updates

Variable, depending on government agencies in each country and other data providers

Date of content

Varies by country

Cautions

This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.

Overview

“Wood fiber concession” refers to an area allocated by a government or other body for establishment of fast-growing tree plantations for the production of timber and wood pulp for paper and paper products.

This data set displays wood fiber concessions as a single layer assembled by aggregating concession data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. For more information on concession data for each country please visit the Open Data Portal.

If you are aware of concession data for additional countries, please email us here.

This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of current or planned wood fiber plantations in Indonesia. This data set is known to be incomplete, but it is compiled from the best information currently available.

This data set provides the boundaries for eucalyptus and other plantations in the Republic of the Congo. The World Resources Institute compiled information from the Republic of the Congo Ministry of Agriculture to produce this data layer.

Credit: World Resources Institute; Republic of the Congo Ministry of Agriculture.

Overview

This data set provides the boundaries for eucalyptus and other plantations in the Republic of the Congo. The World Resources Institute compiled information from the Republic of the Congo Ministry of Agriculture to produce this data layer.

Credit:World Resources Institute; Republic of the Congo Ministry of Agriculture.

Overview

This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of current or planned wood fiber plantations in Indonesia. This data set is known to be incomplete, but it is compiled from the best information currently available.

Shows the distribution of Licenses for Planted Forests (generally for timber trees) in Sarawak

Geographic coverage

Sarawak, Malaysia

Source data

Earthsight Investigations & Global Witness

Date of content

2011

Cautions

Timber plantation licenses issued after January 2011 are not included on this map. Licenses and boundaries which are included may also have been cancelled or amended since that date. Ownership of individual licensees may also have changed since the date when such attribution information was obtained. Official total areas and plantable areas may also have changed..

Overview

This data set provides the boundaries of known Licenses for Planted Forests (LPFs) within Sarawak, Malaysia. LPFs have a maximum tenure of 60 years, and must be over 1,000 hectares in size. Though they are designed for the purpose of planting of timber trees (mostly acacia), licensees are permitted to plant a portion of their licensed area with oil palm trees for a single rotation.

The data set combines the boundaries of LPFs issued up to and included on the Sarawak Forestry Department (SFD) LPF map published 11th Jan 2011. Precise boundaries were sourced from regional SFD maps from 2010 and from Environmental Impact Assessment (EIA) reports for individual concessions.

Licenses (and licensee subsidiaries) have been attributed to major corporate groups based on published company records and secondary sources. Where information on the areas within LPFs allotted for oil palm is available, the boundaries of these areas have been plotted and included in the separate dataset on oil palm concessions in Sarawak. Information on the permit issuance date, official total area in hectares, and plantable area in hectares for LPF licenses has been obtained directly from EIA reports or from secondary sources which have drawn on such reports. Information on area of each LPF license planted up to 2012 (where provided) has been obtained from official published reports or websites of relevant companies.

It should be noted that it is common for LPF licenses to overlap with current Timber Licenses (see separate logging concession dataset for Sarawak). In such instances, LPF clearance and planting only proceeds in individual zones within the LPF when the Timber License holder has completed its own activities.

This data is not official data from the Indonesian Ministry of Environmental and Forestry. As a result, this data may differ from official data and there may be inaccuracies.

Overview

The Leuser Ecosystem spans the provinces of Aceh and North Sumatra on the island of Sumatra in Indonesia. Over 35 times the size of Singapore, this majestic and ancient ecosystem covers more than 2.6 million hectares of lowland rainforests, peat swamps, montane and coastal forests and alpine meadows. Globally recognized as one of the richest expanses of tropical rainforest found anywhere in Southeast Asia, the Leuser Ecosystem is also one of Asia’s largest carbon sinks. The Leuser Ecosystem is the last place on earth where orangutans, rhinos, elephants, and tigers co-exist in the wild. All four of these species are now classified by the International Union for Conservation of Nature (IUCN) as Critically Endangered. The Leuser Ecosystem is the only remaining habitat left in Sumatra large enough to sustain viable populations of these species. A publication in leading international journal Science listed the Leuser Ecosystem as “one of the world’s foremost irreplaceable areas.”

The Leuser Ecosystem is an essential asset for the economic development of Aceh, providing a total economic value of at least 350 million US dollars per year. The Leuser Ecosystem acts as a life-support system for approximately four million people in Aceh. The primary ecosystem services are fresh water provision and disaster mitigation. The forests of the Leuser Ecosystem act as a sponge, soaking up the downpours of the rainy season and spreading out the release of water downstream more evenly across the months. Deforestation of this environmentally sensitive area is having a dramatic impact by increasing the damage caused by flooding and landslides, and causing economic damage to communities and downstream industry. Locally and globally, the Leuser Ecosystem also has immense environmental value due to its role in climate regulation and carbon storage. Efforts to conserve the Leuser Ecosystem date as far back as the early 19th century, when the traditional leaders of Aceh lobbied the colonial government to protect their natural heritage, ranging from the mountains all the way down to the coast. More recent laws have served to strengthen the protection of the Leuser Ecosystem and placed the responsibility for managing its protection and restoration with the Aceh Provincial Government (Article 150 of National Law on Governing Aceh No. 11/2006). Furthermore, the Leuser Ecosystem in Aceh has special legal status as a National Strategic Area for its Environmental Protection Function (26 of 2007 juncto 26/2008), prohibiting any activities that reduce that function, including cultivation and infrastructure development.

Overview

Displays boundaries of areas allocated by the forest authority and supervised by the Supervisory Body for Forest and Wildlife Resources (OSINFOR) for timber, non-timber products, conservation, ecotourism, wildlife, afforestation, and reforestation. The concession grants the licensee the exclusive right to the sustainable use of natural resources granted under the conditions and limitations established by the respective title. The concession grants the holder the right to use and enjoy the natural resource granted and, consequently, ownership of the fruits and products extracted.

Types of concessions are indicated in the attribute data of each boundary, and include:

Timber Concessions - Areas granted for timber operations in production forests permanently established in primary or secondary forests. Granted in two categories: between 5,000-10,000 hectares or areas greater than 10,000 hectares, both renewable for up to forty years.

Non-Timber Forest Products - Areas granted for harvesting of non-timber products, such as fruits, buds, latex, resins, gums, flowers, fibers, whose extraction does not lead to the removal of forest cover. Maximum area of 10,000 hectares, renewable up to forty years.

Conservation Concessions - Concessions aimed at directly contributing to conservation of plant and wildlife through protection and compatible uses such as research, education, and ecological restoration. Logging for timber is not allowed.

Ecotourism Concessions - Concession for the development of activities related to recreation and ecotourism, contributing to the conservation of nature, animals and cultural values of this site, and allowing for beneficial social and economic participation of the local communities. Commercial logging is not permitted. Areas valid for forty years for a maximum area of 10,000 hectares.

Wildlife Concessions - Public lands granted for wildlife management and aimed at sustainable enjoyment of authorized species. Renewable for up to twenty-five years.

Overview

Displays boundaries of areas allocated by the forest authority and supervised by the Supervisory Body for Forest and Wildlife Resources (OSINFOR) for timber, non-timber products, conservation, ecotourism, wildlife, afforestation, and reforestation. The concession grants the licensee the exclusive right to the sustainable use of natural resources granted under the conditions and limitations established by the respective title. The concession grants the holder the right to use and enjoy the natural resource granted and, consequently, ownership of the fruits and products extracted.

Types of concessions are indicated in the attribute data of each boundary, and include:

Timber Concessions - Areas granted for timber operations in production forests permanently established in primary or secondary forests. Granted in two categories: between 5,000-10,000 hectares or areas greater than 10,000 hectares, both renewable for up to forty years.

Non-Timber Forest Products - Areas granted for harvesting of non-timber products, such as fruits, buds, latex, resins, gums, flowers, fibers, whose extraction does not lead to the removal of forest cover. Maximum area of 10,000 hectares, renewable up to forty years.

Conservation Concessions - Concessions aimed at directly contributing to conservation of plant and wildlife through protection and compatible uses such as research, education, and ecological restoration. Logging for timber is not allowed.

Ecotourism Concessions - Concession for the development of activities related to recreation and ecotourism, contributing to the conservation of nature, animals and cultural values of this site, and allowing for beneficial social and economic participation of the local communities. Commercial logging is not permitted. Areas valid for forty years for a maximum area of 10,000 hectares.

Wildlife Concessions - Public lands granted for wildlife management and aimed at sustainable enjoyment of authorized species. Renewable for up to twenty-five years.

Overview

Displays boundaries of areas allocated by the forest authority and supervised by the Supervisory Body for Forest and Wildlife Resources (OSINFOR) for timber, non-timber products, conservation, ecotourism, wildlife, afforestation, and reforestation. The concession grants the licensee the exclusive right to the sustainable use of natural resources granted under the conditions and limitations established by the respective title. The concession grants the holder the right to use and enjoy the natural resource granted and, consequently, ownership of the fruits and products extracted.

Types of concessions are indicated in the attribute data of each boundary, and include:

Timber Concessions - Areas granted for timber operations in production forests permanently established in primary or secondary forests. Granted in two categories: between 5,000-10,000 hectares or areas greater than 10,000 hectares, both renewable for up to forty years.

Non-Timber Forest Products - Areas granted for harvesting of non-timber products, such as fruits, buds, latex, resins, gums, flowers, fibers, whose extraction does not lead to the removal of forest cover. Maximum area of 10,000 hectares, renewable up to forty years.

Conservation Concessions - Concessions aimed at directly contributing to conservation of plant and wildlife through protection and compatible uses such as research, education, and ecological restoration. Logging for timber is not allowed.

Ecotourism Concessions - Concession for the development of activities related to recreation and ecotourism, contributing to the conservation of nature, animals and cultural values of this site, and allowing for beneficial social and economic participation of the local communities. Commercial logging is not permitted. Areas valid for forty years for a maximum area of 10,000 hectares.

Wildlife Concessions - Public lands granted for wildlife management and aimed at sustainable enjoyment of authorized species. Renewable for up to twenty-five years.

The validation of the map was done using field data as well as through a grid of control points spread 3 km apart for the entire country (15,777 points total). The overall accuracy of the map was determined to be 90.9% over all 26 categories, with an accuracy of 95% for the eight forest classes.

The administrative boundaries of the basemap are from Open Street Map and only used to coordinate with other layers in the map.

Overview

This data set is Honduras’ first high-resolution forest and land cover map, produced by the Honduran government agency ICF (National Institute of Conservation and Forest Development, Protected Areas, and Wildlife) with the technical and financial support of Project REDD/CCAD-GIZ. RapidEye satellite imagery from 2013 was acquired and analyzed to produce a map of the spatial distribution of forest types within the country. In addition to mapping of eight different forest types, the map also portrays the extent and distribution of fifteen categories of human land use.

In contrast to earlier forest maps of Honduras, this version expands on other categories of wooded vegetation, including secondary vegetation, coffee and agroforestry areas, and information on trees outside of forests. Pastures and agricultural fields are also aggregated into one category and comprise the second largest land cover class.

Overview

Logging roads are often a first step to deforestation and forest degradation, but they are generally remote and sporadically used, making them difficult to monitor. The Logging Roads initiative, launched by Moabi and Global Forest Watch, uses crowdsourcing and tools developed by OpenStreetMap and the Humanitarian OpenStreetMap Team (HOT) to identify and monitor the spread of logging roads in the Congo Basin. Volunteers use satellite imagery to trace roads and identify the date the road appears.

Data results are biased towards public available data, so gaps may exist.

Overview

The State of the World's Rivers is an interactive web database that illustrates data on ecological health in the world’s 50 major river basins. Indicators of ecosystem health are grouped into the categories of river fragmentation, biodiversity, and water quality. The database was created and published by International Rivers in 2014.

The Dam Hotspots data contains over 5,000 dam locations determined by latitude and longitude coordinates. These locations were confined to the world’s 50 major river basins. The data set comes from multiple sources, and was corrected for location errors by International Rivers. The “project status”—a moving target—was determined by acquiring official government data, as well as through primary research from Berkeley and five International Rivers’ regional offices.

Operational: Already existing dams.

Under construction: Dams which are currently being constructed.

Planned: Dams whose studies or licensing have been completed, but construction has yet to begin.

Inventoried: Dams whose potential site has been selected, but neither studies nor licensing have occurred.

Suspended: Dams which have been temporarily or permanently suspended, deactivated, cancelled, or revoked.

Displays areas that are legally protected according to various designations (e.g., national parks, state reserves, and wildlife reserves) and managed to achieve conservation objectives

Geographic coverage

Global

Source data

The World Database on Protected Areas, which compiles protected area data from governments, NGOs, and international secretariats

Frequency of updates

Monthly

Date of content

Varies by protected area

Cautions

Protected area designations, such as “National Park,” can be applied differently in different countries. Therefore, the associated IUCN category and its description of protection may also vary by country.

Protected areas with no boundary data are displayed as brown dotted boxes, which represent the reported protected area size. The box is centered around a single point location and the borders do not indicate the real boundary of the protected area.

Overview

The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set on marine and terrestrial protected areas available. Protected area data are provided via protectedplanet.net, the online interface for the World Database on Protected Areas (WDPA). The WDPA is a joint initiative of the IUCN and UNEP-WCMC to compile spatially referenced information about protected areas.

Not all protected areas receive the same degree of protection. While some have strict guidelines designed to preserve intact ecosystems, others allow for sustainable land use, often including limited resource extraction. In addition, not all countries use the same terminology when designating a protected area. Accordingly, the International Union for Conservation of Nature defined universal management categories that stipulate the level of protection for most protected areas.

As you click through protected areas in this layer, note the “legal designation” and the explanations below to better understand the degree to which an area is protected.

Ia. Strict Nature Reserves. Protected areas designed to preserve biodiversity and all geological features. Limited human use (e.g., scientific study, education) is allowed and carefully monitored. Strict Nature Reserves are often used to understand the impact of indirect human disturbance (e.g., burning fossil fuels) because of the area’s high level of preservation. Other common designations: Biological Reserve, Botanical Reserve

Ib. Wilderness Areas. Protected areas managed to preserve ecosystem processes with limited human use. Wilderness Areas cannot contain modern infrastructure (e.g., a visitor’s center), but they allow for local indigenous groups to maintain subsistence lifestyles. These areas are often established to restore disturbed environments. Other common designations: Wilderness Reserve, Wildlife Area

II. National Parks. Protected areas designed to preserve large-scale ecosystems and support human visitation. With conservation as a priority, these areas allow infrastructure and contribute to the local economy by providing opportunities for environmental educational and recreation. Other common designations: State Park, Class A Park, Park Reserve, Provincial Park

III. National Monuments or Features. Areas established to protect a specific natural feature (e.g., cave, grove) or human-made monument with significant historical, spiritual, or environmental importance and the immediate surroundings. Accordingly, Natural Monuments or Features are typically smaller in area and have high human impact resulting from visitor traffic. Other common designations: Natural Features Reserve, Nature Monument, Botanical Garden

IV. Habitat and Species Management Areas. Areas designed to conserve specific wildlife populations and/or habitats. Habitat and Species Management Areas often exist within a larger ecosystem or protected area and are carefully managed (e.g., through hunting abatement or habitat restoration) to conserve a target species or habitat. Other common designations: National Wildlife Refuge, State Wildlife Management Area, Faunal Reserve, Zakaznik (Russia), Provincial Reserve, Wildlife Sanctuary

V. Protected Landscapes and Seascapes. Protected areas with ecological, biological, or cultural importance that have been shaped by human use of the landscape. Protected landscapes and seascapes typically cover entire bodies of land or ocean and allow for a number of for-profit activities (e.g., ecotourism) in accordance with the region’s management plan. Other common designations: National Forest, State Natural Area, Environmental Protection Area, Protected Area, Quasi National Park (Japan), Nature Reserve, State Natural Area

VI. Protected Areas with Sustainable Use of Natural Resources. Areas designed to manage natural resources and uphold the livelihoods of surrounding communities. These regions have a low level of human occupation, small-scale developments (i.e., not industrial), and part of the landscape in its natural condition. Other common designations: Wildlife Reserve, Biosphere Reserve, Forest Reserve, Protective Zone, National Forest, Natural and National Reserves, Reserve, Multiple Use Reserve, Municipal Reserve

Other Important Designations

UNESCO-MAP Biosphere Reserves: areas under UNESCO’s Man and the Biosphere Programme designated to “promote sustainable development based on local community efforts and sound science.”

World Heritage Sites: areas considered to have “outstanding universal value” and meet at least one of ten criteria, as described here.

Displays areas that are legally protected according to various designations (e.g., national parks, state reserves, and wildlife reserves) and managed to achieve conservation objectives

Geographic coverage

Global

Source data

The World Database on Protected Areas, which compiles protected area data from governments, NGOs, and international secretariats

Frequency of updates

Monthly

Date of content

Varies by protected area

Cautions

Protected area designations, such as “National Park,” can be applied differently in different countries. Therefore, the associated IUCN category and its description of protection may also vary by country.

Protected areas with no boundary data are displayed as brown dotted boxes, which represent the reported protected area size. The box is centered around a single point location and the borders do not indicate the real boundary of the protected area.

Overview

The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set on marine and terrestrial protected areas available. Protected area data are provided via protectedplanet.net, the online interface for the World Database on Protected Areas (WDPA). The WDPA is a joint initiative of the IUCN and UNEP-WCMC to compile spatially referenced information about protected areas.

Not all protected areas receive the same degree of protection. While some have strict guidelines designed to preserve intact ecosystems, others allow for sustainable land use, often including limited resource extraction. In addition, not all countries use the same terminology when designating a protected area. Accordingly, the International Union for Conservation of Nature defined universal management categories that stipulate the level of protection for most protected areas.

As you click through protected areas in this layer, note the “legal designation” and the explanations below to better understand the degree to which an area is protected.

Ia. Strict Nature Reserves. Protected areas designed to preserve biodiversity and all geological features. Limited human use (e.g., scientific study, education) is allowed and carefully monitored. Strict Nature Reserves are often used to understand the impact of indirect human disturbance (e.g., burning fossil fuels) because of the area’s high level of preservation. Other common designations: Biological Reserve, Botanical Reserve

Ib. Wilderness Areas. Protected areas managed to preserve ecosystem processes with limited human use. Wilderness Areas cannot contain modern infrastructure (e.g., a visitor’s center), but they allow for local indigenous groups to maintain subsistence lifestyles. These areas are often established to restore disturbed environments. Other common designations: Wilderness Reserve, Wildlife Area

II. National Parks. Protected areas designed to preserve large-scale ecosystems and support human visitation. With conservation as a priority, these areas allow infrastructure and contribute to the local economy by providing opportunities for environmental educational and recreation. Other common designations: State Park, Class A Park, Park Reserve, Provincial Park

III. National Monuments or Features. Areas established to protect a specific natural feature (e.g., cave, grove) or human-made monument with significant historical, spiritual, or environmental importance and the immediate surroundings. Accordingly, Natural Monuments or Features are typically smaller in area and have high human impact resulting from visitor traffic. Other common designations: Natural Features Reserve, Nature Monument, Botanical Garden

IV. Habitat and Species Management Areas. Areas designed to conserve specific wildlife populations and/or habitats. Habitat and Species Management Areas often exist within a larger ecosystem or protected area and are carefully managed (e.g., through hunting abatement or habitat restoration) to conserve a target species or habitat. Other common designations: National Wildlife Refuge, State Wildlife Management Area, Faunal Reserve, Zakaznik (Russia), Provincial Reserve, Wildlife Sanctuary

V. Protected Landscapes and Seascapes. Protected areas with ecological, biological, or cultural importance that have been shaped by human use of the landscape. Protected landscapes and seascapes typically cover entire bodies of land or ocean and allow for a number of for-profit activities (e.g., ecotourism) in accordance with the region’s management plan. Other common designations: National Forest, State Natural Area, Environmental Protection Area, Protected Area, Quasi National Park (Japan), Nature Reserve, State Natural Area

VI. Protected Areas with Sustainable Use of Natural Resources. Areas designed to manage natural resources and uphold the livelihoods of surrounding communities. These regions have a low level of human occupation, small-scale developments (i.e., not industrial), and part of the landscape in its natural condition. Other common designations: Wildlife Reserve, Biosphere Reserve, Forest Reserve, Protective Zone, National Forest, Natural and National Reserves, Reserve, Multiple Use Reserve, Municipal Reserve

Other Important Designations

UNESCO-MAP Biosphere Reserves: areas under UNESCO’s Man and the Biosphere Programme designated to “promote sustainable development based on local community efforts and sound science.”

World Heritage Sites: areas considered to have “outstanding universal value” and meet at least one of ten criteria, as described here.

Displays Conservation International’s biodiversity hotspots—defined regions around the world where biodiversity conservation is most urgent because of high levels of endemism and human threat

Geographic coverage

Global (land only)

Frequency of updates

Updated as new data becomes available through Conservation International

Date of content

2011

Cautions

This layer only displays the land-based portion of biodiversity hotspots, although some hotspots extend offshore

Overview

First defined in 1988 by scientist Norman Myers, biodiversity hotspots are areas characterized by high levels of endemic plants coupled with significant habitat loss. Specifically, a region must meet the following criteria to achieve Conservation International’s hotspot classification:

At least 1,500 species of vascular plants (>0.5% of the world’s total) are endemic

At least 70% of the original natural vegetation has been lost

When Myers first defined the term, he identified 10 tropical forest hotspots. The need to pinpoint priority conservation regions led Conservation International (CI) to adopt the term and reassess the hotspot concept. In this process, CI introduced quantitative thresholds (see above) and added additional regions. At that time, there were 25 hotspots. Because of the constant change in environmental threats and the improved understanding of biodiversity, CI has since revisited the hotspots to refine boundaries, update information, and add new regions. This process produced an additional 10 hotspots, bringing the total to 35.

Most ecoregions contain habitats that differ from their assigned biome (e.g., for example, rainforest ecoregions in Amazonia often contain small edaphic savannas).

Overview

Terrestrial Ecoregions of the World (TEOW) is a data set of the geographic distribution Earth's terrestrial biodiversity. Ecoregions are defined as relatively large units of land or water containing a distinct assemblage of natural communities sharing a large majority of species, environmental conditions, and processes, such as migrations or fire disturbance regimes. The 867 terrestrial ecoregions are classified into 14 different biomes, such as forests, grasslands, or deserts. Ecoregions represent the original distribution of distinct assemblages of species and communities.

The TEOW data set provides:

A map of terrestrial biodiversity that gives enough detail to be useful in global and regional conservation priority-setting and planning efforts

This data set contains all of the data that is currently available to Open Development Cambodia (ODC) and is not exhaustive. The development landscape is constantly changing, and there are also additional developments for which data is not available. While ODC takes every effort to ensure that the details in this map are accurate and up to date, some of the protected areas marked on the map may have since been downsized or degazetted since the map was published. Moreover, additional protected areas may have been approved that are not yet included here.

Overview

A protected area is a defined space given extra protection to support long-term conservation of wildlife, nature, ecosystems and cultures. The 1993 Royal Decree on the Protection of Natural Areas recognized 23 protected areas, which at the time covered over 18% of Cambodia’s land area. The royal decree establishes four categories: national park, wildlife sanctuary, protected landscape, and multi-purpose (multiple use) areas necessary for the stability of the water, forestry, wildlife, and fisheries resource, for pleasure, and for the conservation of nature with a view of assuring economic development. In 2008, the Royal Government of Cambodia adopted the Law on Protected Areas introducing and defining a new zoning system to manage the protected areas in Cambodia.

The law, under the jurisdiction of the Ministry of Environment, underlines the expansion and modification of protected areas categories which additionally include the Ramsar sites, biosphere reserves, natural heritage sites and marine parks. Since the Protected Areas Law was passed, numerous sub-decrees have been passed classifying areas as Sustainable Use Zones (degazettement). According to the law, after consulting with relevant ministries and institutions, local authorities, and local communities, the Government may permit development and investment activities in these zones. These activities can include infrastructure development, including irrigation and hydroelectricity projects, mining and resin exploitation.

This dataset contains data for five types of protected areas in Cambodia (national park, wildlife sanctuary, protected landscape, multiple use, Ramsar site) with issuing dates starting from 1993 to 2014. Data for biosphere reserves, national heritage sites, and marine parks is currently not available on ODC. Due to the lack of publicly available spatial information on zoning and degazettement of protected areas, this dataset does not include information on adjustments.

Open Development Cambodia collected the data from a variety of public domain sources such as the government, non-governmental organizations (NGOs), research institutes, company websites, and news reports.

Citation: Open Development Cambodia. “Protected Areas.” Accessed through Global Forest Watch on [date].www.globalforestwatch.org

This map is known to be incomplete, particularly in relation to proposed protected areas. The boundaries of a significant number of proposed totally protected areas known to exist as of 2012 are not included. A few small gazetted protected areas are also not included. Some areas included in the map which were classified 'proposed' in 2012 may have been cancelled since, while others may also have been gazetted after December 2014.

Overview

There are three classes of Totally Protected Areas (TPAs) in Sarawak: National Parks, Wildlife Sanctuaries and Nature Reserves. Some of the TPAs are gazetted, while others remain ungazetted and are classified as 'proposed'.

All boundaries are from Sarawak Forestry Department (SFD) maps. Three principal map sources were used: a high-resolution SFD map of northern Sarawak from February 2010; a high-resolution SFD map of central Sarawak from 2010; and low-resolution SFD maps of National Parks, Wildlife Sanctuaries and Nature Reserves available on the SFD website and accessed in April 2014. TPAs which are entirely over water are not included.

The status of each protected area given in this data set is based on the list of gazetted TPAs as of 31st December 2014 given on the website of the Sarawak Forestry Corporation (accessed November 2015), and the Sarawak Forestry Department Annual Report of 2012.

Overview

This data set displays the boundaries of six Brazilian continental biomes: the Amazônia, Cerrado, Caatinga, Mata Atlântica, Pantanal and Pampa. “Biome” is defined as a collection of life (plant and animal) constituted by the grouping of contiguous vegetation types identifiable on a regional scale with similar geoclimatic conditions and shared history, which results in a unique biological diversity. The names used were the most common and popular in general associated with the predominant type of vegetation or relief, as in the case of Pantanal biome, which is the highest provincial flooded surface of the world.

The Amazon Biome is defined by the climatic region, forest physiognomy and geographic location. The Atlantic Forest biome, which occupies the entire Brazilian continental east Atlantic coast and stretches inland in the Southeast and South, is defined by the predominant forest vegetation and diverse relief. The Pampa, restricted to Rio Grande do Sul, is defined by a set of field vegetation in plain relief. The predominant vegetation in the Cerrado biome in Brazil, second in size, extends from the Maranhão coast to the Midwest and the Caatinga Biome, typical of semi-arid climate of the northeastern backlands. The map is a result of a partnership between the Brazilian Ministry of Environemnt (MMA) and the Brazilian Institute of Geography and Statistics (IBGE). To read more about the data set, please visit: http://www.ibge.gov.br/home/presidencia/noticias/21052004biomashtml.shtm.

Displays areas where the geographic range of two or more endemic bird species overlaps

Geographic coverage

Global

Source data

BirdLife International

Frequency of updates

Updated Annually

Date of content

2014

Overview

While many bird species are widespread, over 2,500 are endemic and restricted to an area smaller than 5 million hectares (restricted-range species). BirdLife International has mapped every restricted-range species using geo-referenced locality records. Through this process, they identified regions of the world—known as “Endemic Bird Areas” (EBAs)—where the distributions of two or more of these species overlap.

Half of all restricted-range species are globally threatened or near-threatened, and the other half remain vulnerable to loss or degradation of habitat. The majority of EBAs are also important for the conservation of restricted-range species from other animal and plant groups. The unique landscapes where these bird species occur, amounting to just 4.5% of the earth's land surface, are high priorities for broad-scale ecosystem conservation.

Geographically, EBAs are often islands or mountain ranges, and vary considerably in size, from a few hundred hectares to more than 10,000,000 hectares. EBAs also vary in the number of restricted-range species that they support (from two to 80). EBAs are found around the world, but most (77%) of them are located in the tropics and subtropics.

Displays critical sites for conservation that contain endangered species with limited ranges and populations found nowhere else on the planet

Geographic coverage

Global

Source data

The Alliance for Zero Extinction

Frequency of updates

Every 5 years

Date of content

2010; Updated March 16, 2012

Overview

Created by the Alliance for Zero Extinction (AZE), this data set shows 587 sites for 920 species of mammals, birds, amphibians, reptiles, conifers, and reef-building corals. The species found within these sites have extremely small global ranges and populations; any change to habitat within a site may lead to the extinction of a species in the wild. To meet AZE Extinction Site status, a site must:

Contain at least one Endangered or Critically Endangered species

Be the sole area where an Endangered or Critically Endangered species occurs

Contain greater than 95% of either the known resident population of the species or 95% of the known population of one life history segment (e.g. breeding or wintering) of the species

Have a definable boundary (e.g., species range, extent of contiguous habitat, etc.)

Launched in 2005, the Alliance for Zero Extinction (AZE) engages 83 non-governmental biodiversity conservation organizations working to prevent species extinctions. The AZE identifies and safeguards places where species evaluated to be Endangered or Critically Endangered by the International Union for Conservation of Nature are restricted to single remaining sites.

Updated as new data becomes available through Biodiversity & Wildlife Solutions, RESOLVE

Date of content

2007

Cautions

Tiger Conservation Landscapes were created under the assumption that suitable habitat depends on quality and size of land cover and prey base.

Land cover data was problematic in certain geographies due to the presence of tree plantations. In some cases, forest cover was overestimated or underestimated.

The tiger location database, on which this data set was built, is incomplete for some regions, and the data comes from a variety of sources and research methods.

Overview

Tiger Conservation Landscapes (TCLs) are large blocks of contiguous or connected area of suitable tiger habitat that that can support at least five adult tigers and where tiger presence has been confirmed in the past 10 years. The data set was created by mapping tiger distribution, determined by land cover type, forest extent, and prey base, against a human influence index. Areas of high human influence that overlapped with suitable habitat were not considered tiger habitat.

Tigers require a large area to survive. Accordingly, habitat loss is a major cause of the species’ rapid decline. Before this data set, many countries containing tigers did not have spatially explicit tiger habitat maps necessary to develop habitat conservation and management plans. Among others, this information gap was an impetus to developing the TCL data set.

Overview

This data set displays 29 Tx2 Tiger Conservation Landscapes (Tx2 TCLs), defined areas that could double the wild tiger population through proper conservation and management by 2020.

The number of wild tigers has declined from an estimated 100,000 in the early 1900s to a current estimate of around 3,500 adult animals. In response to this rapid decline, government officials convened in November 2010 to endorse the St. Petersburg Declaration, pledging to double the wild tiger population by 2020. To aid in this effort, Wikramanayake and his team conducted a landscape analysis of tiger habitat to determine if a recovery of such magnitude is possible. They identified 29 Tiger Conservation Landscapes with potential for doubling wild tiger population with proper conservation and management.

The corridors are existing forests or grasslands which might also include human settlements.

Overview

This data set displays 9 forest corridors on the Nepalese side of the Terai Arc Landscape (TAL). Corridors are defined as existing forests connecting current Royal Bengal tiger meta-populations in Nepal and India.

The TAL is spread over 4.95 million hectares, linking 14 transboundary protected areas across Nepal and India. This landscape has the second largest population of rhinos, one of the highest densities of tiger populations, and is home to the Asiatic elephant. In Nepal, TAL encompasses 2.31 million hectares extending over 14 districts and includes 75 percent of the remaining forests of lowland Nepal. In addition, TAL was recognized as a WWF Global 200 ecoregion and spans three Ramsar sites and two World Heritage Sites.

New VCS REDD+ projects will be added on a quarterly basis as they become registered under VCS

Date of content

Varies by project area and is specified in the records of each individual VCS project

Cautions

Not all VCS REDD+ project areas are included on this map. For a full list of VCS projects and their respective areas, please visit the VCS Project Database.

License

Not all VCS REDD+ project areas are included on this map. For a full list of VCS projects and their respective areas, please visit the VCS Project Database.

Overview

The Verified Carbon Standard (VCS) is the world’s most widely used voluntary greenhouse gas (GHG) emission reduction program. VCS projects are developed across a wide range of sectoral scopes, including those classified within the Agriculture, Forestry and Other Land Use (AFOLU) sector. These projects reduce emissions from forest-related activities around the world and apply robust GHG accounting methodologies to quantify such emission reductions, which are independently verified and transparently registered.

This layer shows VCS projects categorized as REDD (Reduced Emissions from Deforestation and Forest Degradation), IFM (Improved Forest Management) and ARR (Afforestation, Reforestation and Revegetation). More detail on the VCS program and these project categories can be found in the VCS Standard and VCS AFOLU Requirements documents.

In addition to these project activities, more than a dozen national and subnational jurisdictions around the world are applying the VCS Jurisdictional and Nested REDD+(JNR) framework to account for the emission reductions generated by their REDD+ policies and measures. This information will be added in the future as these programs register under the VCS.

Maps the boundaries of conservation easements on private lands and provides information on their status and management

RESOLUTION / SCALE

Variable

Geographic coverage

United States

Source data

Various – Government, Land Trusts, NGOs, other

Frequency of updates

As needed

Date of content

2014

Cautions

The NCED completeness maps measure the number of easements that are in the NCED database compared to the estimated total number of easements the NCED team is aware of from the Land Trust Alliance Census data and the NCED data collection efforts. Easements are known yet not in NCED because, 1) they have not been digitized, 2) they were withheld from NCED, or 3) the NCED team is still working with the easement holders to collect the information.

Approximately 70% of known land trusts are included in the Census; however, the Census gives us a good base number for comparison with NCED. There is no census for publicly-held easements so the total number of publicly-held easements is based on the data collection efforts of NCED.

Overview

A conservation easement, according to the Land Trust Alliance, is “a legal agreement between a landowner and a land trust or government agency that permanently limits uses of the land in order to protect its conservation values.” The National Conservation Easement Database (NCED) is the first national database of conservation easements in the United States. Voluntary and secure, the NCED respects landowner privacy and will not collect landowner names or sensitive information. This public-private partnership brings together national conservation groups, local and regional land trusts, and state and federal agencies around a common objective. The NCED provides a comprehensive picture of the estimated 40 million acres of privately owned conservation easement lands, recognizing their contribution to America’s natural heritage, a vibrant economy, and healthy communities.

Before the NCED was created no single, nationwide system existed for sharing and managing information about conservation easements. By building the first national database and web site to access this information, the NCED helps agencies, land trusts, and other organizations plan more strategically, identify opportunities for collaboration, advance public accountability, and raise the profile of what's happening on-the-ground in the name of conservation.

Boundaries are subject to change from rezoningLa cobertura es susceptible a modificaciones en sus límites a partir de redimensionamientos

Overview

Permanent production forests are areas of natural primary forest that, under a ministerial resolution of the Ministry of Agriculture, are available to private interests for the preferential use of wood and other forest resources as well as wildlife as proposed by the forest and wildlife authority.

In these areas, use rights for different products of wood and wildlife may be granted, as long as they don’t affect the long-term potential of said resources.

Updated when a new buffer zone is establishedActualizado cuando una nueva zona de amortiguamiento es establecida

Date of contentFECHA DE LOS CONTENIDOS

July 2015Julio 2015

LICENSELICENCIA

May not be modified from the original data.Prohibido modificar la información original.

Overview

Buffer zones are areas adjacent to natural protected areas (PAs) that, because of their nature and location, require special treatment to guarantee the conservation of the PA. The activities in the buffer zone should not put the objectives of the PA at risk. All use of natural resources in the buffer zone requires prior approval by SERNANP.

Updated when a national protected area is modified or established.Actualizado cuando un área protegida a nivel nacional es establecida o modificada.

Date of contentFECHA DE LOS CONTENIDOS

March 2015Marzo 2015

LICENSELICENCIA

May not be modified from the original dataProhibido modificar la información original

Overview

This layer shows protected areas that are managed by the national government, under the jurisdiction of the National Service of Natural Protected Areas (SERNANP). These areas are divided in three categories of use: indirect use, direct use, and reserved zone.

Areas of indirect use

In indirect areas, the extraction of natural resources or other types of environmental modification are not allowed. These areas only permit non-manipulative scientific investigation and tourist, recreational, educational, and cultural activities under regulated conditions. The following are indirect use areas:

National Parks: created in areas that represent the large ecological units in the country. In these areas, SERNANP protects the ecological integrity of one or more ecosystems, the associations of wild plants and animals, successional and evolutionary processes, as well as landscape and cultural characteristics. In this areas, hunting, ranching, farming, timber, or mining activities, or any activity that exploits natural resources, cannot be developed.

National Sanctuaries: areas where the habitat of a species or a community of flora or fauna is protected, as natural formations of scientific interest and national importance.

Historical Sanctuaries: areas that in addition to protecting natural values, show the the monumental and archaeological heritage of the country, or are areas where outstanding historical events took place.

Areas of direct use

In direct use areas, the use of natural resources is allowed, primarily by local people, under the guidelines of a Management Plan approved and supervised by the relevant national authority. The following are direct use areas:

National Reserves: areas dedicated to biodiversity conservation and sustainable use, including commercial use, of wild flora and fauna under management plans, with the exception of commercial forest use for timber

Landscape Reserves: areas where environments with geographical integrity are protected, housing important natural, cultural, and aesthetic values. If the prior zonification allows it, these areas can permit traditional use of natural resources, scientific investigation, tourism, and human settlements. Activities that cause notable changes in the landscape and the value of the area are excluded

Protected Forests: areas that are established to protect important watersheds, the banks of rivers and other water bodies, and stop erosion of fragile lands. In this areas, the use of resources and the development of activities are allowed, provided that those activities do not affect vegetation cover, fragile soils, or water flow.

Communal Reserves: areas dedicated to the conservation of wild flora and fauna for the benefit of neighboring rural people who are using resources traditionally. The use and commercialization of resources is done under management plans, approved and supervised by the government authority and conducted by the beneficiary populations.

Hunting Preserves: areas dedicated to the use of wild fauna through the regulated practice of sport hunting.

Wildlife Refuges: areas that require active intervention to guarantee the maintenance and recuperation of habitats and populations of particular species. These areas exclude commercial use of natural resources that could significantly change the habitat.

Reserved Zones:

In addition to the above categories, Reserved Zones are established as a transitory category in areas that have the conditions necessary to be considered as a natural protected area, but that require additional study to determine their extension and category, among other things.

Citation:“Peru national protected areas”. National Service of Natural Protected Areas (SERNANP). 2015. Accessed through Global Forest Watch on [date]. www.globalforestwatch.org

May not be modified from the original data.Prohibido modificar la información original.

Overview

This layer shows conservation areas that are created partially or totally on private property. The environmental, biological, or scenic properties of this land are complementary to the coverage of national PAs, supporting biodiversity conservation and increasing the opportunities for scientific investigation, education, and tourism. Lands located inside of buffer zones are prioritized for private PAs.

The recognition of private PAs is based in an agreement between the State and the owner of the land with the objective of conserving biodiversity for a renewable 10+ year period. A breach of the obligations of the agreement on the part of the land owner results in the loss of recognition as a private PA.

May not be modified from the original data.Prohibido modificar la información original.

Overview

This layer shows protected areas that are managed by regional governments. The laws established for national protected areas also apply, where relevant, to regional protected areas. Regional protected areas are not divided into categories, but this does not mean that their conservation objectives are all the same.

Regional protected areas are managed in coordination with municipalities, native or rural communities, other people that live in the area, and public and private institutions. Administration of regional protected areas can be delegated, with prior permission of the regional government, to individuals with interest and the ability to manage the area.

Regional protected areas respect pre-existing rights within their interior, but these activities should be compatible with the goal of national heritage.

People

Data compiled under “People” indicate areas over which indigenous peoples or local communities have rights over land and/or certain resources.

The laws of many countries separate land rights from resource rights. Legally recognized land rights extend to the land and certain resources associated with the land, depending on the nature of the right. The right to the land and certain resources include some combination of rights of access, use, management, exclusion, and alienation. Similarly, legally recognized resource rights can cover some combination of the right to access, use, manage, exclude, or alienate forests, wildlife, or other resources. Whether rights to the land or resources, the law may recognize their rights in perpetuity or for a limited period of time.

Some data sets displayed on Global Forest Watch include land and resource rights governed by customary tenure systems but that are not recognized by national laws.

Land Rights(select countries)

Function

Displays boundaries of areas over which indigenous peoples or local communities enjoy rights to the land and certain resources

RESOLUTION / SCALE

Varies by country

Geographic coverage

Currently available for Australia, Brazil, Canada, Costa Rica, New Zealand, and Panama

Source data

Generally based on a combination of sources, including government agencies, NGOs and other organizations. For information on country-specific concessions data please refer to the Data page.

Frequency of updates

Variable, depending on government agencies in each country and other data providers

Date of content

Varies by country

Cautions

Some data sets displayed on Global Forest Watch include land and resource rights governed by customary tenure systems but that are not recognized by national laws.

Overview

“Land Rights” refers to areas over which indigenous peoples or local communities enjoy rights to the land and certain resources, whether legally recognized or not. The exact nature of these land rights varies among tenure type and country.

The land rights data on GFW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.

Land rights data displayed on the GFW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations.

This data set displays the boundaries of areas designated as Indigenous Lands in Brazil. Indigenous Lands legally recognize indigenous peoples’ perpetual rights of access, use, withdrawal, management, and exclusion over the land and associated resources. Alienation of the land is prohibited. However, commercial use of forest resources is permitted, but cutting trees for sale requires approval by the National Legislature. Rights to subsoil resources may be obtained only with the approval of the National Legislature and after consultation with the affected indigenous peoples. This data set includes Indigenous Lands that are officially registered and those at various stages of the registration process.

The Aboriginal Lands data set depicts the administrative boundaries (exterior limits) of lands where the title has been vested in specific Aboriginal Groups of Canada or lands which were set aside for their exclusive benefit. The Aboriginal Lands data set includes, but is not limited to, Indian Reserves,Cree-Naskapi Category 1A and 1A-N Lands, Yukon First Nation Settlement Lands, Kanesatake Mohawk Interim Land Base, the Inuit Owned Lands, Tlicho Lands, Inuvialuit Lands, Gwich’in Lands and Sahtu Lands.

Credit:Government of Canada, Natural Resources Canada, Earth Sciences Sector, Geomatics Canada, Surveyor General Branch. Available through the Open Government License - Canada

Not currently available for download

This data set displays the boundaries of areas designated as comarcas in Panama. Comarcas are legally recognized semi-autonomous areas where indigenous peoples own the land and resources with rights of access, use, withdrawal, management, and exclusion. Although the Government retains ownership of subsoil resources, the indigenous community must be consulted by government and private organizations for proposed developments on their lands. The government and mining concessionaire are required to guarantee benefits to the community and compliance with sustainable development practices.

Some countries provide data for the entire country (Bolivia, Ecuador, French Guiana, Guyana, and Suriname), while others only provide land rights datafor the Amazon portion of their country (Brazil, Colombia, Peru, and Venezuela).

Unfortunately, RAISG data is not downloadable or analyzable on Global Forest Watch at this time.

Overview

RAISG is a network of organizations with the goal of sharing georeferenced socioeconomic data throughout the Amazon Basin. This data set shows the boundaries of indigenous territories in Bolivia, the Brazilian Amazon, the Colombian Amazon, Ecuador, French Guiana, Guyana, the Peruvian Amazon, Suriname, and the Venezuelan Amazon. The territories are devided into four categories of legal recognition: "Traditional occupation without legal recognition", "Proposed territorial reserve", "Territorial reserve or intangible zone" and "Recognized indigenous land". Unfortunately, RAISG data is not downloadable or analyzable on Global Forest Watch at this time. To see the data in the original web map, click

Overview

The inalienable Aboriginal freehold lands data set represents boundary and attribute information for land parcels, granted to incorporated Aboriginal groups through the Aboriginal Land Rights (Northern Territory) Act of 1976, which are greater than 40 hectares. Aboriginal land is private property owned under special freehold title. It is inalienable, meaning it cannot be bought, acquired, or forfeited. The inalienable Aboriginal freehold is granted as a communal title (where land is held collectively by a group, rather than by individuals), and is the strongest form of title in Australia. This title gives Aboriginal groups the power to control the direction and pace of development on their land. The data set only includes Aboriginal lands that are officially registered. More information is available at: http://www.ga.gov.au/metadata-gateway/metadata/record/42339/.

Overview

This data set displays the boundaries of areas designated as comarcas in Panama. Comarcas are legally recognized semi-autonomous areas where indigenous peoples own the land and resources with rights of access, use, withdrawal, management, and exclusion. Although the Government retains ownership of subsoil resources, the indigenous community must be consulted by government and private organizations for proposed developments on their lands. The government and mining concessionaire are required to guarantee benefits to the community and compliance with sustainable development practices.

Overview

This data set displays the boundaries of areas designated as indigenous lands in Brazil. Indigenous lands legally recognize indigenous peoples’ perpetual rights of access, use, withdrawal, management, and exclusion over the land and associated resources. Alienation of the land is prohibited. However, commercial use of forest resources is permitted, but cutting trees for sale requires approval by the National Legislature. Rights to subsoil resources may be obtained only with the approval of the National Legislature and after consultation with the affected indigenous peoples. This data set includes indigenous lands that are officially registered and those at various stages of the registration process.

Overview

The aboriginal lands data set depicts the administrative boundaries (exterior limits) of lands where the title has been vested in specific aboriginal groups of Canada or lands which were set aside for their exclusive benefit. The aboriginal lands data set includes, but is not limited to, Indian Reserves, Cree-Naskapi Category 1A and 1A-N Lands, Yukon First Nation Settlement Lands, Kanesatake Mohawk Interim Land Base, the Inuit Owned Lands, Tlicho Lands, Inuvialuit Lands, Gwich’in Lands, and Sahtu Lands.

Credit:Government of Canada, Natural Resources Canada, Earth Sciences Sector, Geomatics Canada, Surveyor General Branch. Available through the Open Government License - Canada

Overview

This data set displays the boundaries of the 24 legally-recognized and titled indigenous territories in Costa Rica as of 2008. It was created by the Observatorio del Desarrollo within the Universidad de Costa Rica, and is made available through the online Digital Atlas of Indigenous Peoples. Data sources include the Universidad de Costa Rica, la Universidad Nacional, and el Instituto Tecnológico. To view the interactive Atlas, please visit pueblosindigenas.odd.ucr.ac.cr/

Overview

This data set displays the boundaries of areas designated as Māori Lands. These areas include both Māori freehold lands and Māori customary lands. Māori customary lands belong to the Crown but are communally held by Māori in accordance with customary values and practices, generally referred to as tikanga Māori. Māori freehold lands are Māori customary lands that have been converted, through application to the Māori Land Court, to the titled, private possession of Māori individuals or small groups (fewer than five owners). Most Māori land falls under the category of Māori freehold land. The Native Lands Act of 1862 created the Māori Land Court, which oversees the administration of freehold land titles.

Displays boundaries of areas over which indigenous peoples or local communities enjoy rights to certain resources and a limited right to access the land

RESOLUTION / SCALE

Varies by country

Geographic coverage

Currently available for Cameroon, Equatorial Guinea, Liberia and Namibia

Source data

Generally based on a combination of sources, including government agencies, NGOs and other organizations. For information on country-specific concessions data please refer to the Data page.

Frequency of updates

Variable, depending on government agencies in each country and other data providers

Date of content

Varies by country

Cautions

Some data sets displayed on Global Forest Watch include land and resource rights governed by customary tenure systems but that are not recognized by national laws.

Overview

“Resource Rights” refers to areas over which indigenous peoples or local communities enjoy rights to certain resources and a limited right to access the land, whether legally recognized or not, in order to exercise their resource rights. The exact nature of these resource rights varies among tenure type and country.

The resource rights data on GFW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.

Resource rights data displayed on the GFW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations. See the Open Data Portal for details on specific data sets.

If you are aware of resource rights data for additional countries, please email us here.

This data set displays the boundaries of areas designated as Community forests in Cameroon. Community forests legally recognize a community’s ownership rights to forest resources, both timber and non-timber. It includes the right to access, use, withdraw for commercial purposes or subsistence, and exclude others from the forest. The land remains owned by the Cameroonian Government. The community’s rights to forest resources are renewed every five years as long as the community complies with the Community Forest Management Agreement. A community may also contract with a third party to commercially harvest timber.

This data set displays the boundaries of areas designated as Community forests in Equatorial Guinea. Community forests legally recognize a community’s right to access government owned land in order to use the forest for subsistence. The forest remains owned by the government, and must be adjacent to the community. Community forests are of perpetual duration.

Credit: WRI Congo Basin Forest Atlas

Currently unavailable for download

Communal Forests are areas set aside by statute or regulation for the sustainable use of forest products by local communities or tribes on a non-commercial basis. According to the National Forestry Reform Law of 2006, no prospecting, mining, settlement, farming or commercial timber extraction is permitted on community forests.

This data set displays the boundaries of areas registered as Community forests in Namibia. Community forests are recognized by the Minister of Environment and Tourism as communal lands subject to a management plan agreed upon by the Minister and a representative body of communal land members. In accordance with the agreement, the management plan grants communal land members the rights to manage and use natural resources, including the removal of forest produce for fuel, personal shelter, or livestock shelter; allows for agricultural activity; allows communal land members to authorize others to use the community forest’s natural resources; and allows communal land members to collect a fee and set conditions for the use of natural resources.

Credit:Namibian Association of Community Based Natural Resource Management (CBNRM) Support Organisations (NACSO)

Cameroon community forests

Overview

This data set displays the boundaries of areas designated as Community forests in Cameroon. Community forests legally recognize a community’s ownership rights to forest resources, both timber and non-timber. It includes the right to access, use, withdraw for commercial purposes or subsistence, and exclude others from the forest. The land remains owned by the Cameroonian Government. The community’s rights to forest resources are renewed every five years as long as the community complies with the community forest management agreement. A community may also contract with a third party to commercially harvest timber.

Overview

Communal forests are areas set aside by statute or regulation for the sustainable use of forest products by local communities or tribes on a non-commercial basis. According to the National Forestry Reform Law of 2006, no prospecting, mining, settlement, farming or commercial timber extraction is permitted on community forests.

Credit:USAID-Liberia PROSPER

This data set is not available for download

Equatorial Guinea community forests

Overview

This data set displays the boundaries of areas designated as community forests in Equatorial Guinea. Community forests legally recognize a community’s right to access government owned land in order to use the forest for subsistence. The forest remains owned by the government, and must be adjacent to the community. Community forests are of perpetual duration.

Overview

This data set displays the boundaries of areas registered as community forests in Namibia. Community forests are recognized by the Minister of Environment and Tourism as communal lands subject to a management plan agreed upon by the Minister and a representative body of communal land members. In accordance with the agreement, the management plan grants communal land members the rights to manage and use natural resources, including the removal of forest produce for fuel, personal shelter, or livestock shelter; allows for agricultural activity; allows communal land members to authorize others to use the community forest’s natural resources; and allows communal land members to collect a fee and set conditions for the use of natural resources.

Credit:Namibian Association of Community Based Natural Resource Management (CBNRM) Support Organisations (NACSO)

Overview

“The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 in a 1km global grid. The population density grids measure population per square km.

A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population values to grid cells. The population count grids are divided by the land area grid to produce population densities. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

Stories

Stories represent qualitative or anecdotal data on forests, submitted by users or written and compiled from other sources.

User Stories

This layer displays forest-related stories reported by GFW users. Stories are tagged to a specific location and can include photos, video, or explanatory text. See all GFW stories or report your own here.

Mongabay Stories

This layer displays stories from Mongabay.com, a leading environmental science and conservation news website. Stories published within one year of the current date are tagged to a specific location and provide in-depth research or commentary on local forest issues. More information can be found at Mongabay.com

Earth Journalism Network Stories

This layer displays stories sourced from the Earth Journalism Network, a project of Internews that empowers and enables journalists from developing countries to cover the environment more effectively. Earth Journalism Network provides geo-tagged, syndicated stories through regional platforms, including InfoAmazonia (the Amazon), InfoCongo (the Congo Basin), and Ekuatorial (Indonesia). Stories are tagged to a specific location and provide in-depth research or commentary on local forest issues. More information can be found at Earth Journalism Network.

Base maps

Base maps provide a variety of map backgrounds for visual comparison with other data.

Default

The default base map is derived from Google Maps and shows political boundaries, major geological features, and other key areas of interest. Read the terms of service here.

Google Maps’ satellite base map consists of a mix of recent (1–3 years old) mid-resolution and high-resolution satellite and aerial imagery from multiple providers for a given area. TerraMetrics TruEarth 15-meter imagery is the baselayer imagery that covers the entire globe, and Google adds high-resolution imagery, where available, over TruEarth 15-meter imagery to provide additional visual details. Read the terms of service here.

Hybrid

Google Maps Road Network base map shows the extent of collected and generated road features. The map is proprietary to Google and cannot be downloaded. Scale varies by location. Read the terms of service here.

Tree Height

This base map depicts the highest points in the forest canopy. Its spatial resolution is 0.6 miles (1 km) and was validated against data from a network of nearly 70 ground sites around the world. It was developed by a team of scientists from NASA’s Jet Propulsion Laboratory, Pasadena, California; the University of Maryland, College Park; and Woods Hole Research Center, Falmouth, Massachusetts. The map was created using 2.5 million carefully screened, globally distributed laser pulse measurements from space. The light detection and ranging (Lidar) data were collected in 2005 by the Geoscience Laser Altimeter System instrument on NASA’s Ice, Cloud, and land Elevation Satellite (ICESat).

Open Street Map is a free, editable map of the whole world that is crowd-sourced and released with an open-content license. Data can be edited or downloaded at http://www.openstreetmap.org/

Landsat Composites

These base maps show a composite of the best annual Landsat satellite imagery (USGS/NASA) from 1999 to 2012. The annual composites were generated by Google in the Google Earth Engine. The Landsat composites display “Top-of-Atmosphere,” or the most cloud-free, images at 30-meter resolution. More information on Landsat imagery is available from the Landsat website. Read the terms of service here.

Note: Because of the very large file size of the annual Landsat composite layers, images will take some time to reload on the website and as you zoom in and out.

Countries

Data provided on the country pages come from a range of sources. Read below to learn more about each.

Food and Agriculture Organization of the United Nations (FAO)

Many figures on the country pages come from the FAO’s Global Forest Resources Assessment (FRA). As stated on the FAO website, the FRA is a comprehensive assessment of forests and forestry that examines the current status and recent trends for different variables covering the extent, condition, uses and values of forests. To create the report, information was collated from 233 countries and territories for four points in time: 1990, 2000, 2005 and 2010. FAO worked closely with countries and specialists in the design and implementation of FRA 2010 - through regular contact, expert consultations, training for national correspondents and ten regional and subregional workshops. More than 900 contributors were involved, including 178 officially nominated national correspondents and their teams. To learn more, visit the FAO website or download the full report.

The country pages also present data from FAOSTAT, FAO’s platform for data collection and dissemination. According to FAO, the FAOSTAT Emissions Land Use database provides country-level estimates of greenhouse gas (GHG) emissions based on FAOSTAT activity data using Tier 1 computations, following 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National GHG Inventories. Changes in carbon stocks and ecosystem function linked to anthropogenic activities such as land-use change and land management determine emissions and removals of GHG that are reported by countries for the IPCC Land Use, Land-Use Change and Forestry (LULUCF) categories. To learn more, visit the FAOSTAT website.

Data on the economic value and employment in national forestry sectors are reported from “Contribution of the forestry sector to national economies, 1990-2006” by A. Lebedys. Forest Finance Working Paper FSFM/ACC/08. FAO, Rome. To learn more, download the full report.

European Space Agency (GlobCover)

Data from the European Space Agency’s GlobCover was used to visualize tree cover on the country pages. As stated on the ESA website, GlobCover is an initiative which began in 2005 in partnership with a number of organizations. The aim of the project was to develop a service capable of delivering global composites and land cover maps using input observations from the 300m MERIS sensor on board the ENVISAT satellite mission. ESA makes available the land cover maps, which cover 2 periods: December 2004 - June 2006 and January - December 2009. To learn more, visit the GlobCover website or download the 2009 GlobCover map.

Figures: Map of tree cover

The Rights and Resources Initiative (RRI)

Figures on forest tenure come from the Rights and Resource Initiative and are based on data adapted from the Tropical Forest Tenure Assessment. The goal of the report is to present and analyze the state of forest tenure in much of the world’s tropical forests. As stated on RRI's website, the tenure systems represented by this data is sourced from governments, and therefore only reflects those systems of natural resource management that are legally recognized by those governments. Such officially outlined tenure systems fall under the category of statutory tenure regimes within these studies. The official data often presents an incomplete picture of the institutions that actually manage natural resources, particularly at a local level. To learn more, visit the RRI forest tenure page.

Figures: Forest tenure

Code

The Global Forest Watch website and Global Forest Watch API are both collaborative and open-source projects hosted on GitHub. If you would like to contribute source code, please send pull requests to our website repository or API repository. All Global Forest Watch source code is released under The MIT License (MIT).

Additionally, the tropical forest carbon stocks layer is being stored and served from Google Maps Engine and styled by Earth Engine, the Imazon SAD data are created using Imazon's algorithms on Earth Engine, and analyses of the Hansen/UMD/Google/USGS/NASA data, as well as computations of area, are being performed by Earth Engine.

Sign up here to access the full suite of Earth Engine tools and data, including data available on Global Forest Watch, 40+ years of Landsat data, many additional satellite data sets, elevation data, atmospheric data, and data you upload yourself. For information about how to analyze Global Forest Watch data, please visit our tutorial.

GFW on ArcGIS Online

View Global Forest Watch’s ArcGIS Online page to browse for downloadable data, maps, and spatial data resources. View our data, overlay our data sets with your own, or browse any of the available data layers available through ArcGIS Online. With a free ArcGIS Online public account you can create, store, and manage maps, apps, and data, and share them with others.

Data policy & standards

Learn more about Global Forest Watch’s commitment to high quality, open data.

Data standards

Global Forest Watch is committed to providing the highest-quality data on the world’s forests that is currently available. All of the data provided on GFW have been assessed with respect to several indicators of quality, including timeliness, accuracy, completeness, geographic coverage, innovation, and objectivity.

In some cases, the best available data still have known shortcomings with respect to one or more of these indicators. In those cases, we provide a “caution” explaining known issues or inaccuracies.

Global Forest Watch has an open data policy and commits to make our data, and that of our partners, open, discoverable, downloadable, and usable. We believe that open data enables the innovation, information sharing, analysis, and transparency required to support policies that better manage and sustain the world’s forests.

Open data policy

Global Forest Watch has an open data policy, intended to provide information free of constraints and restrictions on use. All of the data, graphics, charts and other material we produce carry the Creative Commons CC BY 4.0 licensing. This means you are able to download, share, and adapt the data for any use, including commercial and noncommercial uses. You must attribute the data appropriately, using the information provided in the data set description.

Some data displayed on the Global Forest Watch platform was developed by other organizations and may carry other licensing or permissions. For data sets not produced by GFW, we provide links to download data from the original source when they are available. Please refer to the original licensing for these data sets.

Most of the photographs featured on this site have either been licensed under non-transferable terms, or have been acquired from photo sharing sites that have their own policies for public use. Unless indicated otherwise, the Creative Commons license described above does not apply to photographs and images used on this site.

Data sharing

Terms of use

For data sets produced by GFW, you are free to use, distribute, copy, modify, and display the data for commercial and noncommercial purposes.

You are free to use maps, graphics, charts, and other representations of data on the GFW website, with the Creative Commons CC BY 4.0 license.

Licensing is determined for each individual data set—please refer to the licensing text for detailed information.

You must attribute the data as indicated in the metadata or licensing for each individual data set.

You must not imply that Global Forest Watch endorses your use of the data, or use the Global Forest Watch logo in conjunction with such use.

Through accessing the GFW site you have acknowledged and agreed to our Terms of Service.

Permissions & licensing

Except as noted below, all material on this site carries the Creative Commons CC BY 4.0 license, which permits unrestricted reuse of GFW content when proper attribution is provided. This means you are free to copy, display, and distribute GFW’s work, or include our content in derivative works, under the following conditions:

Attribution. You must clearly attribute the work and provide a link back to the work on www.globalforestwatch.org. Doing so ensures that people access the latest available information, in the event that the publication is revised or more research is published. Click here for the full legal code of Creative Commons CC BY 4.0 license. Some content on this site may carry additional copyright restrictions. GFW has made every effort to clearly label such content.

Photographs. Most of the photographs featured on this site have either been licensed under nontransferable terms, or have been acquired from photo-sharing sites such as Flickr that have their own policies for public use. Unless indicated otherwise, the Creative Commons CC BY 4.0 license described above does not apply to photographs and images used on this site.

Charts, Graphs, & Maps. Charts, graphs, and maps produced by GFW may be used according to the Creative Commons CC BY 4.0 license. Where graphs employ data from third parties, we may not be able to grant permissions to the original data used in these charts, although such permissions may be granted by the owners of the data. In these cases, please contact the third-party organizations directly to ask about permissions. If you have any questions about citing or reusing GFW content, please visit the How to page or contact us.

Explore the map

Use the colored tabs above the map to view different categories of data. Select a data layer to activate it in the map, or click the information icon located next to each data layer to learn more.

As you turn on data layers, they will appear in the legend on the left-hand side of the map. Multiple data layers can be turned on at once.

Most data layers under the Forest Change tab have a timeline located at the bottom of the map. Press the play button to view an animation of the entire time series of data or drag the ends of the timeline to select a specific period of time.

1. Using the tabs above the map, turn on the Forest Change data layer you wish to analyze (e.g., tree cover loss).

2. Define the time period of your analysis by dragging the handles of the timeline or by selecting a time interval.

3. Select the analysis icon () on the right-hand side of the map and choose “Country or region."

4. Choose a country and/or region and click “Analyze.”

Learn more about analyzing forest change data within a country or subnational jurisdiction on the How To page.

Visualize data for your country of interest

1. Select a country using the window on the right-hand side of the map.

2. If available, country data layers will then appear. Click on the available layers within the window or within the country tab above the map to turn them on.

Learn more about analyzing forest change data within a country or subnational jurisdiction on the How To page.

CHANGE THE PERIOD OF ANALYSIS

Interested in forest change statistics for a different time period? Define the time period you wish to analyze by dragging the handles of the timeline on the map or (where applicable) by selecting a time interval.

Interested in forest change statistics for a different time period? Define the time period you wish to analyze by selecting a time interval or date using the bar located on the bottom of the map.

View existing global data

Use the tabs above the map to turn on GFW's global data layers. Click the information icons in the drop down menus to learn more about each data set.

Please note that some global layers, such as those under the "Forest Use" and "People" tabs, contain data for a select number of countries and are not truly "global" in nature. Rather, these data sets are a compilation of individual country data aggregated into joint layers (e.g., oil palm concessions).

Return to the data layers menu and select "Global data" to turn on one of GFW's global data layers on the map. Click the information icons in the drop down menus to learn more about each data set.

Please note that some global layers, such as those under the "Forest Use" and "People" tabs, contain data for a select number of countries and are not truly "global" in nature. Rather, these data sets are a compilation of individual country data aggregated into joint layers (e.g., oil palm concessions).

ANALYZE A SHAPE ON THE MAP

This feature allows you to analyze forest change statistics for individual data shapes (i.e, polygons) within certain layers. Analysis is currently available for most data layers under the Land Use, Conservation, and People tabs, in addition to all country data layers. Turn on the layer you wish to analyze and follow the instructions.

For more help analyzing a data shape on the map, visit the How To page.

SUBSCRIBE TO A SHAPE ON THE MAP

This feature allows you to subscribe to forest change alerts and user stories for individual data shapes (i.e, polygons) within certain layers. Subscription is currently available for most data layers under the Land Use, Conservation, and People tabs, in addition to all country data layers. Turn on the layer you wish to subscribe to and follow the instructions.

For more help subscribing a data shape on the map, visit the How To page.

UPLOAD A CUSTOM DATA SET

Drop a file in the designated area to analyze or subscribe to it. The recommended maximum file size is 1MB. Anything larger than that may not work properly.

NOTE: This feature counts alerts or hectares inside of polygons; therefore, only polygon data is supported, not point and line data. Please ensure that your file only contains polygon data. A maximum of 1,000 feautures can be analysed in a single upload, contained within a single layer. Multiple layers are not supported.

Recent Imagery

Overview

This layer shows recent satellite imagery, as provided by Google Earth Engine, from the following sensors:

Sensor

Owner

Resolution

Revisit Time

Minimum Date

Geographic Coverage

Sentinel-2

ESA

10 m

every 10 days

June 2015

Global

Hover on an image to see the capture date and sensor name. The most recent image for an area is shown, click to view other images or change the time period. Users can also change the maximum cloud cover percentage in the menu, which sets the threshold for excluding cloudy images.

Renderer

Satellites can “see” wavelengths of light that the human eye cannot detect, but that can give more information about the Earth’s surface. Sentinel hub provides the following renderings of the images:

RGB (Red Green Blue): uses information on red, green, and blue light to show the Earth as humans see it.

Normalized Difference Vegetation Index (NDVI): uses information on red and infrared light to estimate the health of vegetation. This index is mapped from a white to green hue, where green pixels indicate good crop health and white pixels indicate poor crop health or an absence of vegetation.

Enhanced Vegetation Index (EVI): another vegetation index using red and infrared information. It is more sensitive to variations in canopy structure than NDVI, and is better at controlling background noise. EVI is not available for Deimos-1 imagery.

Normalized Difference Water Index (NDWI): uses information on green and infrared light to help detect the presence of water in vegetation. The calculated pixel values range from blue to brown - where blue indicates the presence of water, and brown indicates dryer land. It is another tool to determine crop health and condition.

False color NIR (Near infrared): shows near infrared light as red, and visible light as green and blue. Vegetation reflects infrared light, so it generally shows up as bright red.

Sensor

Select which sensor you would like to view imagery from. Each sensor has a different resolution, revisit time, minimum date, and geographic coverage. See the table below for details.

Sensor

Owner

Resolution

Revisit Time

Minimum Date

Geographic Coverage

Landsat 8

USGS

30 m

every 16 days

March 2013

Global

Sentinel-2

ESA

10 m

every 10 days

June 2015

Global

The default selection, "all sensors," will show the most recent image that meets other specified criteria, such as maximum cloud coverage percentage, for any given area.

MAXIMUM CLOUD COVER PERCENTAGE

Move the slider to adjust the maximum percentage of cloud cover for available imagery. For example, if you select 25%, only images with less than 25% cloud cover will be displayed.

AQUIRED DATE MINIMUM / MAXIMUM

Minimum: Images displayed will have been acquired after this date

Maximum: Images displayed will have been acquired before this date.

Due to variation in research methodology and/or date of content, tree cover and tree cover loss and gain statistics cannot be compared against each other. Accordingly, “net” loss cannot be calculated by subtracting tree cover gain from tree cover loss, and current (or post-2000) tree cover cannot be determined by subtracting annual tree cover loss from year 2000 tree cover.

Please also be aware that “tree cover” does not equate to “forest cover.” “Tree cover” refers to the biophysical presence of trees, which may be a part of natural forests or tree plantations. Thus, loss of tree cover may occur for many reasons, including deforestation, fire, and logging within the course of sustainable forestry operations. Similarly, tree cover gain may indicate the growth of tree canopy within natural or managed forests.

Use our WebHooks to receive deforestation alerts in a machine readable format.

How does it work?

A WebHook is an HTTP callback: an HTTP POST that occurs when something happens. Global Forest Watch will POST a message to a URL on your webserver when new alerts are available for the area of interest you subscribed to. The payload of the POST request contains all of your subscription's information in a machine readable format. Your webserver must be capable of accepting POST requests at the given URL. You can implement your own logic on your webserver to process the incoming alerts and include them directly in your application.

What information do I receive?

We attach a JSON blob as a payload to the POST request. The blob includes general metadata for your subscription, the time period covered, the number of alerts, as well as a link to the GFW map. Depending on the type of subscription, you may receive additional information.

For fire and deforestation alerts, this includes:

A link to an image showing all of the alerts on map

A list of coordinate pairs representing the alerts themselves

For user story alerts, this includes:

Title and description of the story

A link to an attached image

A link to the story itself

We currently only send you the 10 most recent alerts. If there are more than 10 alerts, we will provide a link where you can download all alerts in CSV or GeoJSON format.